def trpo(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
steps_per_epoch=4000, epochs=50, gamma=0.99, delta=0.01, vf_lr=1e-3,
train_v_iters=80, damping_coeff=0.1, cg_iters=10, backtrack_iters=10,
backtrack_coeff=0.8, lam=0.97, max_ep_len=1000, logger_kwargs=dict(),
save_freq=10, algo='trpo'):
"""
Trust Region Policy Optimization
(with support for Natural Policy Gradient)
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
============ ================ ========================================
Symbol Shape Description
============ ================ ========================================
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp`` (batch,) | Gives log probability, according to
| the policy, of taking actions ``a_ph``
| in states ``x_ph``.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``.
``info`` N/A | A dict of any intermediate quantities
| (from calculating the policy or log
| probabilities) which are needed for
| analytically computing KL divergence.
| (eg sufficient statistics of the
| distributions)
``info_phs`` N/A | A dict of placeholders for old values
| of the entries in ``info``.
``d_kl`` () | A symbol for computing the mean KL
| divergence between the current policy
| (``pi``) and the old policy (as
| specified by the inputs to
| ``info_phs``) over the batch of
| states given in ``x_ph``.
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``. (Critical: make sure
| to flatten this!)
============ ================ ========================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to TRPO.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
delta (float): KL-divergence limit for TRPO / NPG update.
(Should be small for stability. Values like 0.01, 0.05.)
vf_lr (float): Learning rate for value function optimizer.
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
damping_coeff (float): Artifact for numerical stability, should be
smallish. Adjusts Hessian-vector product calculation:
.. math:: Hv \\rightarrow (\\alpha I + H)v
where :math:`\\alpha` is the damping coefficient.
Probably don't play with this hyperparameter.
cg_iters (int): Number of iterations of conjugate gradient to perform.
Increasing this will lead to a more accurate approximation
to :math:`H^{-1} g`, and possibly slightly-improved performance,
but at the cost of slowing things down.
Also probably don't play with this hyperparameter.
backtrack_iters (int): Maximum number of steps allowed in the
backtracking line search. Since the line search usually doesn't
backtrack, and usually only steps back once when it does, this
hyperparameter doesn't often matter.
backtrack_coeff (float): How far back to step during backtracking line
search. (Always between 0 and 1, usually above 0.5.)
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
algo: Either 'trpo' or 'npg': this code supports both, since they are
almost the same.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space, env.action_space)
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
# Main outputs from computation graph, plus placeholders for old pdist (for KL)
pi, logp, logp_pi, info, info_phs, d_kl, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, adv_ph, ret_ph, logp_old_ph] + core.values_as_sorted_list(info_phs)
# Every step, get: action, value, logprob, & info for pdist (for computing kl div)
get_action_ops = [pi, v, logp_pi] + core.values_as_sorted_list(info)
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
info_shapes = {k: v.shape.as_list()[1:] for k,v in info_phs.items()}
buf = GAEBuffer(obs_dim, act_dim, local_steps_per_epoch, info_shapes, gamma, lam)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n'%var_counts)
# TRPO losses
ratio = tf.exp(logp - logp_old_ph) # pi(a|s) / pi_old(a|s)
pi_loss = -tf.reduce_mean(ratio * adv_ph)
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Optimizer for value function
train_vf = MpiAdamOptimizer(learning_rate=vf_lr).minimize(v_loss)
# Symbols needed for CG solver
pi_params = core.get_vars('pi')
gradient = core.flat_grad(pi_loss, pi_params)
v_ph, hvp = core.hessian_vector_product(d_kl, pi_params)
if damping_coeff > 0:
hvp += damping_coeff * v_ph
# Symbols for getting and setting params
get_pi_params = core.flat_concat(pi_params)
set_pi_params = core.assign_params_from_flat(v_ph, pi_params)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'pi': pi, 'v': v})
def cg(Ax, b):
"""
Conjugate gradient algorithm
(see https://en.wikipedia.org/wiki/Conjugate_gradient_method)
"""
x = np.zeros_like(b)
r = b.copy() # Note: should be 'b - Ax(x)', but for x=0, Ax(x)=0. Change if doing warm start.
p = r.copy()
r_dot_old = np.dot(r,r)
for _ in range(cg_iters):
z = Ax(p)
alpha = r_dot_old / (np.dot(p, z) + EPS)
x += alpha * p
r -= alpha * z
r_dot_new = np.dot(r,r)
p = r + (r_dot_new / r_dot_old) * p
r_dot_old = r_dot_new
return x
def update():
# Prepare hessian func, gradient eval
inputs = {k:v for k,v in zip(all_phs, buf.get())}
Hx = lambda x : mpi_avg(sess.run(hvp, feed_dict={**inputs, v_ph: x}))
g, pi_l_old, v_l_old = sess.run([gradient, pi_loss, v_loss], feed_dict=inputs)
g, pi_l_old = mpi_avg(g), mpi_avg(pi_l_old)
# Core calculations for TRPO or NPG
x = cg(Hx, g)
alpha = np.sqrt(2*delta/(np.dot(x, Hx(x))+EPS))
old_params = sess.run(get_pi_params)
def set_and_eval(step):
sess.run(set_pi_params, feed_dict={v_ph: old_params - alpha * x * step})
return mpi_avg(sess.run([d_kl, pi_loss], feed_dict=inputs))
if algo=='npg':
# npg has no backtracking or hard kl constraint enforcement
kl, pi_l_new = set_and_eval(step=1.)
elif algo=='trpo':
# trpo augments npg with backtracking line search, hard kl
for j in range(backtrack_iters):
kl, pi_l_new = set_and_eval(step=backtrack_coeff**j)
if kl <= delta and pi_l_new <= pi_l_old:
logger.log('Accepting new params at step %d of line search.'%j)
logger.store(BacktrackIters=j)
break
if j==backtrack_iters-1:
logger.log('Line search failed! Keeping old params.')
logger.store(BacktrackIters=j)
kl, pi_l_new = set_and_eval(step=0.)
# Value function updates
for _ in range(train_v_iters):
sess.run(train_vf, feed_dict=inputs)
v_l_new = sess.run(v_loss, feed_dict=inputs)
# Log changes from update
logger.store(LossPi=pi_l_old, LossV=v_l_old, KL=kl,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
agent_outs = sess.run(get_action_ops, feed_dict={x_ph: o.reshape(1,-1)})
a, v_t, logp_t, info_t = agent_outs[0][0], agent_outs[1], agent_outs[2], agent_outs[3:]
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# save and log
buf.store(o, a, r, v_t, logp_t, info_t)
logger.store(VVals=v_t)
# Update obs (critical!)
o = o2
terminal = d or (ep_len == max_ep_len)
if terminal or (t==local_steps_per_epoch-1):
if not(terminal):
print('Warning: trajectory cut off by epoch at %d steps.'%ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = 0 if d else sess.run(v, feed_dict={x_ph: o.reshape(1,-1)})
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs-1):
logger.save_state({'env': env}, None)
# Perform TRPO or NPG update!
update()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch+1)*steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('KL', average_only=True)
if algo=='trpo':
logger.log_tabular('BacktrackIters', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
def sac(env_fn, logger_kwargs=dict(), network_params=dict(), rl_params=dict()):
# env params
thresh = rl_params['thresh']
# control params
seed = rl_params['seed']
epochs = rl_params['epochs']
steps_per_epoch = rl_params['steps_per_epoch']
replay_size = rl_params['replay_size']
batch_size = rl_params['batch_size']
start_steps = rl_params['start_steps']
max_ep_len = rl_params['max_ep_len']
save_freq = rl_params['save_freq']
render = rl_params['render']
# rl params
gamma = rl_params['gamma']
polyak = rl_params['polyak']
lr = rl_params['lr']
grad_clip_val = rl_params['grad_clip_val']
# entropy params
alpha = rl_params['alpha']
target_entropy = rl_params['target_entropy']
logger = EpochLogger(**logger_kwargs)
if save_freq is not None:
logger.save_config(locals())
train_env, test_env = env_fn(), env_fn()
obs = train_env.observation_space
act = train_env.action_space
tf.set_random_seed(seed)
np.random.seed(seed)
train_env.seed(seed)
train_env.action_space.np_random.seed(seed)
test_env.seed(seed)
test_env.action_space.np_random.seed(seed)
# get the size after resize
obs_dim = network_params['input_dims']
act_dim = act.shape[0]
# init a state buffer for storing last m states
train_state_buffer = StateBuffer(m=obs_dim[2])
test_state_buffer = StateBuffer(m=obs_dim[2])
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = placeholders(obs_dim, act_dim, obs_dim, None, None)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi, q1_a, q2_a = build_models(x_ph, a_ph, act, act_dim, network_params)
with tf.variable_scope('main', reuse=True):
# compose q with pi, for pi-learning
_, _, _, q1_pi, q2_pi = build_models(x_ph, pi, act, act_dim, network_params)
# get actions and log probs of actions for next states, for Q-learning
_, pi_next, logp_pi_next, _, _ = build_models(x2_ph, a_ph, act, act_dim, network_params)
# Target value network
with tf.variable_scope('target'):
_, _, _, q1_pi_targ, q2_pi_targ = build_models(x2_ph, pi_next, act, act_dim, network_params)
# alpha Params
if target_entropy == 'auto':
target_entropy = tf.cast(-act_dim, tf.float32)
else:
target_entropy = tf.cast(target_entropy, tf.float32)
log_alpha = tf.get_variable('log_alpha', dtype=tf.float32, initializer=0.0)
if alpha == 'auto': # auto tune alpha
alpha = tf.exp(log_alpha)
else: # fixed alpha
alpha = tf.get_variable('alpha', dtype=tf.float32, initializer=alpha)
# Count variables
var_counts = tuple(count_vars(scope) for scope in ['log_alpha',
'main/pi',
'main/q1',
'main/q2',
'main'])
print("""\nNumber of other parameters:
alpha: %d,
pi: %d,
q1: %d,
q2: %d,
total: %d\n"""%var_counts)
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
min_q_pi_targ = tf.minimum(q1_pi_targ, q2_pi_targ)
# Targets for Q and V regression
q_backup = tf.stop_gradient(r_ph + gamma*(1-d_ph)*(min_q_pi_targ - alpha*logp_pi_next))
# critic losses
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1_a)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2_a)**2)
value_loss = q1_loss + q2_loss
# Soft actor losses
pi_loss = tf.reduce_mean(alpha * logp_pi - min_q_pi)
# alpha loss for temperature parameter
alpha_backup = tf.stop_gradient(logp_pi + target_entropy)
alpha_loss = -tf.reduce_mean(log_alpha * alpha_backup)
# Policy train op
pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
if grad_clip_val is not None:
gvs = pi_optimizer.compute_gradients(pi_loss, var_list=get_vars('main/pi'))
capped_gvs = [(ClipIfNotNone(grad, grad_clip_val), var) for grad, var in gvs]
train_pi_op = pi_optimizer.apply_gradients(capped_gvs)
else:
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
# Value train op
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
with tf.control_dependencies([train_pi_op]):
if grad_clip_val is not None:
gvs = value_optimizer.compute_gradients(value_loss, var_list=get_vars('main/q'))
capped_gvs = [(ClipIfNotNone(grad, grad_clip_val), var) for grad, var in gvs]
train_value_op = value_optimizer.apply_gradients(capped_gvs)
else:
train_value_op = value_optimizer.minimize(value_loss, var_list=get_vars('main/q'))
alpha_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
with tf.control_dependencies([train_value_op]):
train_alpha_op = alpha_optimizer.minimize(alpha_loss, var_list=get_vars('log_alpha'))
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([tf.assign(v_targ, polyak*v_targ + (1-polyak)*v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# All ops to call during one training step
step_ops = [pi_loss, q1_loss, q2_loss, q1_a, q2_a, logp_pi, target_entropy, alpha_loss, alpha,
train_pi_op, train_value_op, train_alpha_op, target_update]
# Initializing targets to match main variables
target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
sess = tf.Session(config=tf_config)
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
if save_freq is not None:
logger.setup_tf_saver(sess, inputs={'x_ph': x_ph, 'a_ph': a_ph},
outputs={'mu': mu, 'pi': pi, 'q1_a': q1_a, 'q2_a': q2_a})
def get_action(state, deterministic=False):
state = state.astype('float32') / 255.
act_op = mu if deterministic else pi
return sess.run(act_op, feed_dict={x_ph: [state]})[0]
def reset(env, state_buffer):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
o = process_image_observation(o, obs_dim, thresh)
state = state_buffer.init_state(init_obs=o)
return o, r, d, ep_ret, ep_len, state
def test_agent(n=10, render=True):
for j in range(n):
o, r, d, ep_ret, ep_len, test_state = reset(test_env, test_state_buffer)
if render: test_env.render()
while not(d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
o, r, d, _ = test_env.step(get_action(test_state, True))
o = process_image_observation(o, obs_dim, thresh)
test_state = test_state_buffer.append_state(o)
ep_ret += r
ep_len += 1
if render: test_env.render()
if render: test_env.close()
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len, state = reset(train_env, train_state_buffer)
total_steps = steps_per_epoch * epochs
save_iter = 0
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > start_steps:
a = get_action(state)
else:
a = train_env.action_space.sample()
# Step the env
o2, r, d, _ = train_env.step(a)
o2 = process_image_observation(o2, obs_dim, thresh)
next_state = train_state_buffer.append_state(o2)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len==max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(state, a, r, next_state, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
state = next_state
if d or (ep_len == max_ep_len):
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
outs = sess.run(step_ops, feed_dict)
logger.store(LossPi=outs[0], LossQ1=outs[1], LossQ2=outs[2],
Q1Vals=outs[3], Q2Vals=outs[4], LogPi=outs[5], TargEntropy=outs[6],
LossAlpha=outs[7], Alpha=outs[8])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len, state = reset(train_env, train_state_buffer)
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
if save_freq is not None:
if (epoch % save_freq == 0) or (epoch == epochs-1):
logger.save_state({'env': train_env}, itr=save_iter)
save_iter+=1
# Test the performance of the deterministic version of the agent.
test_agent(n=2, render=render)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('LogPi', average_only=True)
logger.log_tabular('TargEntropy', average_only=True)
logger.log_tabular('Alpha', average_only=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossAlpha', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
def bcq_learn(env_set="Hopper-v2", seed=0, buffer_type="FinalSigma0.5_env_0_1000K",
batch_size=100, eval_freq=int(1e2), max_timesteps=float(2e6), lr=1e-3,
save_freq=int(1e2),
logger_kwargs=dict()):
eval_freq = save_freq if eval_freq=="save_freq" else eval_freq
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("running on device:", device)
"""set up logger"""
global logger
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
file_name = "BCQbatchpolicy_%s_%s" % (env_set, seed)
print("---------------------------------------")
print
("Task: " + file_name)
print("Evaluate Policy every", eval_freq * batch_size / 1e6,
'epoches; Total', max_timesteps * batch_size / 1e6, 'epoches')
print("---------------------------------------")
if not os.path.exists("./results"):
os.makedirs("./results")
env = gym.make(env_set)
test_env = gym.make(env_set)
# Set seeds
env.seed(seed)
test_env.seed(seed)
env.action_space.np_random.seed(seed)
test_env.action_space.np_random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
# Initialize policy
policy = BCQ_batchpolicy.BCQ(state_dim, action_dim, max_action, lr=lr)
# Load buffer
if 'sac' in buffer_type:
replay_buffer = utils.BEAR_ReplayBuffer()
desire_stop_dict = {'Hopper-v2': 1000, 'Walker2d-v2': 500, 'HalfCheetah-v2': 4000, 'Ant-v2': 750}
buffer_name = buffer_type.replace('env', env_set).replace('crt', str(desire_stop_dict[env_set]))
replay_buffer.load(buffer_name)
buffer_name += '_1000K'
#setting_name = setting_name.replace('crt', str(desire_stop_dict[env_set]))
elif 'Final' in buffer_type or 'sigma'in buffer_type:
replay_buffer = utils.ReplayBuffer()
buffer_name = buffer_type.replace('env', env_set)
replay_buffer.load(buffer_name)
elif 'optimal' in buffer_type:
buffer_name = buffer_type.replace('env', env_set)
replay_buffer = utils.ReplayBuffer()
replay_buffer.load(buffer_name)
else:
raise FileNotFoundError('! Unknown type of dataset %s' % buffer_type)
training_iters, epoch = 0, 0
while training_iters < max_timesteps:
epoch += eval_freq * batch_size / 1e6
bcq_state_dict = policy.train(replay_buffer, iterations=int(eval_freq), batch_size=batch_size, logger=logger)
if (training_iters % save_freq == 0):
logger.save_state(bcq_state_dict, training_iters)
avgtest_reward = evaluate_policy(policy, test_env)
training_iters += eval_freq
logger.log_tabular('Epoch', epoch)
logger.log_tabular('AverageTestEpRet', avgtest_reward)
logger.log_tabular('TotalSteps', training_iters)
logger.log_tabular('QLoss', average_only=True)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('ActLoss', with_min_and_max=True)
logger.dump_tabular()
def ppo(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=1e-3,
train_pi_iters=80,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10):
"""
Proximal Policy Optimization (by clipping),
with early stopping based on approximate KL
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: The constructor method for a PyTorch Module with a
``step`` method, an ``act`` method, a ``pi`` module, and a ``v``
module. The ``step`` method should accept a batch of observations
and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``a`` (batch, act_dim) | Numpy array of actions for each
| observation.
``v`` (batch,) | Numpy array of value estimates
| for the provided observations.
``logp_a`` (batch,) | Numpy array of log probs for the
| actions in ``a``.
=========== ================ ======================================
The ``act`` method behaves the same as ``step`` but only returns ``a``.
The ``pi`` module's forward call should accept a batch of
observations and optionally a batch of actions, and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` N/A | Torch Distribution object, containing
| a batch of distributions describing
| the policy for the provided observations.
``logp_a`` (batch,) | Optional (only returned if batch of
| actions is given). Tensor containing
| the log probability, according to
| the policy, of the provided actions.
| If actions not given, will contain
| ``None``.
=========== ================ ======================================
The ``v`` module's forward call should accept a batch of observations
and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``v`` (batch,) | Tensor containing the value estimates
| for the provided observations. (Critical:
| make sure to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
you provided to PPO.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
clip_ratio (float): Hyperparameter for clipping in the policy objective.
Roughly: how far can the new policy go from the old policy while
still profiting (improving the objective function)? The new policy
can still go farther than the clip_ratio says, but it doesn't help
on the objective anymore. (Usually small, 0.1 to 0.3.) Typically
denoted by :math:`\epsilon`.
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_pi_iters (int): Maximum number of gradient descent steps to take
on policy loss per epoch. (Early stopping may cause optimizer
to take fewer than this.)
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
target_kl (float): Roughly what KL divergence we think is appropriate
between new and old policies after an update. This will get used
for early stopping. (Usually small, 0.01 or 0.05.)
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
# Special function to avoid certain slowdowns from PyTorch + MPI combo.
setup_pytorch_for_mpi()
# Set up logger and save configuration
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Random seed
seed += 10000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
# Instantiate environment
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Create actor-critic module
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
# Sync params across processes
sync_params(ac)
# Count variables
var_counts = tuple(core.count_vars(module) for module in [ac.pi, ac.v])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# Set up experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Set up optimizers for policy and value function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
# Prepare for interaction with environment
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v, logp = ac.step(torch.as_tensor(o, dtype=torch.float32))
next_o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# save and log
buf.store(o, a, r, v, logp)
logger.store(VVals=v)
# Update obs (critical!)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len,
flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
_, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32))
else:
v = 0
buf.finish_path(v)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
def update():
# Set up function for computing PPO policy loss
def compute_loss_pi(data):
obs, act, adv, logp_old = data['obs'], data['act'], data[
'adv'], data['logp']
# Policy loss
pi, logp = ac.pi(obs, act)
ratio = torch.exp(logp - logp_old)
clip_adv = torch.clamp(ratio, 1 - clip_ratio,
1 + clip_ratio) * adv
loss_pi = -(torch.min(ratio * adv, clip_adv)).mean()
# Useful extra info
approx_kl = (logp_old - logp).mean().item()
ent = pi.entropy().mean().item()
clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio)
clipfrac = torch.as_tensor(clipped,
dtype=torch.float32).mean().item()
pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac)
return loss_pi, pi_info
# Set up function for computing value loss
def compute_loss_v(data):
obs, ret = data['obs'], data['ret']
return ((ac.v(obs) - ret)**2).mean()
data = buf.get()
pi_l_old, pi_info_old = compute_loss_pi(data)
pi_l_old = pi_l_old.item()
v_l_old = compute_loss_v(data).item()
# Train policy with multiple steps of gradient descent
for i in range(train_pi_iters):
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
kl = mpi_avg(pi_info['kl'])
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' %
i)
break
loss_pi.backward()
mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
logger.store(StopIter=i)
# Value function learning
for i in range(train_v_iters):
vf_optimizer.zero_grad()
loss_v = compute_loss_v(data)
loss_v.backward()
mpi_avg_grads(ac.v) # average grads across MPI processes
vf_optimizer.step()
# Log changes from update
kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf']
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(loss_pi.item() - pi_l_old),
DeltaLossV=(loss_v.item() - v_l_old))
# Perform PPO update!
update()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def vpg_linesearch_penalty(env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
pi_lr=3e-3,
vf_lr=1e-3,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
backtrack_iters=500,
logger_kwargs=dict(),
save_freq=10):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp`` (batch,) | Gives log probability, according to
| the policy, of taking actions ``a_ph``
| in states ``x_ph``.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``.
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``. (Critical: make sure
| to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to VPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space,
env.action_space)
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
# Main outputs from computation graph
pi, logp, logp_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi]
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = VPGBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# VPG objectives
pi_loss = -tf.reduce_mean(logp * adv_ph)
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Info (useful to watch during learning)
approx_kl = tf.reduce_mean(
logp_old_ph -
logp) # a sample estimate for KL-divergence, easy to compute
approx_ent = tf.reduce_mean(
-logp) # a sample estimate for entropy, also easy to compute
# Optimizers
##train_pi is not used
train_pi = MpiAdamOptimizer(learning_rate=pi_lr).minimize(pi_loss)
train_v = MpiAdamOptimizer(learning_rate=vf_lr).minimize(v_loss)
##op for taking a gradient step
## use AdamOptimizer as it is adjusts the learning rate
opt = tf.train.AdamOptimizer(learning_rate=pi_lr)
pi_name = "pi"
scope_variable = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=pi_name)
grads_and_vars = opt.compute_gradients(pi_loss, scope_variable)
pi_grad_step_op = opt.apply_gradients(grads_and_vars)
pi_grad_norm = tf.global_norm([item[0] for item in grads_and_vars])
#policy params - need to get and save policy params
pi_params = core.get_vars('pi')
gradient = core.flat_grad(pi_loss, pi_params)
v_ph = tf.placeholder(tf.float32, shape=gradient.shape)
get_pi_params = core.flat_concat(pi_params)
set_pi_params = core.assign_params_from_flat(v_ph, pi_params)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'pi': pi, 'v': v})
def batch_run(inputs):
penalty = 0
aa, v_t, logp_t = sess.run(get_action_ops, feed_dict=inputs)
for aindex in range(len(aa)):
o, r, d, _ = env.step(aa[aindex])
penalty = penalty + env.penalty_sa(o, aa[aindex])
if d:
o = env.reset()
break
#print("batch_run, size {}, Penaty {} ".format(aindex,env.penalty_sa(o,aa[aindex])))
return penalty
def update():
inputs = {k: v for k, v in zip(all_phs, buf.get())}
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Policy gradient step
#sess.run(train_pi, feed_dict=inputs)
# Value function learning
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
##linesearch - backtracking iteration using penalty
save_params = sess.run(get_pi_params)
for j in range(backtrack_iters):
old_penalty = batch_run(inputs)
pi_l_old = sess.run([pi_loss], feed_dict=inputs)
sess.run(pi_grad_step_op, feed_dict=inputs)
new_penalty = batch_run(inputs)
pi_l_new = sess.run([pi_loss], feed_dict=inputs)
if new_penalty < old_penalty:
#if pi_l_new <= pi_l_old:
#print("Accepting params at iter {} pi_l_new={} pi_l_old={}".format(j, pi_l_new, pi_l_old))
#print("Accepting params at iter {} new_penalty={} old_penalty={}".format(j, new_penalty, old_penalty))
save_params = sess.run(get_pi_params)
sess.run(set_pi_params, feed_dict={v_ph: save_params})
#break
sess.run(set_pi_params, feed_dict={v_ph: save_params})
# Log changes from update
pi_l_new, v_l_new, kl = sess.run([pi_loss, v_loss, approx_kl],
feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v_t, logp_t = sess.run(get_action_ops,
feed_dict={x_ph: o.reshape(1, -1)})
# save and log
buf.store(o, a, r, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(a[0])
ep_ret += r
ep_len += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t == local_steps_per_epoch - 1):
if not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = r if d else sess.run(
v, feed_dict={x_ph: o.reshape(1, -1)})
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform VPG update!
update()
# Log info about epoch
#logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', average_only=True)
#logger.log_tabular('EpLen', average_only=True)
#logger.log_tabular('VVals', with_min_and_max=True)
#logger.log_tabular('TotalEnvInteracts', (epoch+1)*steps_per_epoch)
#logger.log_tabular('LossPi', average_only=True)
#logger.log_tabular('LossV', average_only=True)
#logger.log_tabular('DeltaLossPi', average_only=True)
#logger.log_tabular('DeltaLossV', average_only=True)
#logger.log_tabular('Entropy', average_only=True)
#logger.log_tabular('KL', average_only=True)
#logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
def klucb_bs_sac(env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=5000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=1e-3,
alpha=0.2,
batch_size=100,
start_steps=10000,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``mu`` (batch, act_dim) | Computes mean actions from policy
| given states.
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``. Critical: must be differentiable
| with respect to policy parameters all
| the way through action sampling.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
``q2_pi`` (batch,) | Gives the composition of ``q2`` and
| ``pi`` for states in ``x_ph``:
| q2(x, pi(x)).
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``.
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to SAC.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim,
obs_dim, None, None)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi, q1, q2, q1_pi, q2_pi, v = actor_critic(
x_ph, a_ph, **ac_kwargs)
# Target value network
with tf.variable_scope('target'):
_, _, _, _, _, _, _, v_targ = actor_critic(x2_ph, a_ph, **ac_kwargs)
# mu = tf.squeeze(mu,axis=1)
# pi = tf.squeeze(pi,axis=1)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables
var_counts = tuple(
core.count_vars(scope)
for scope in ['main/pi', 'main/q1', 'main/q2', 'main/v', 'main'])
print(('\nNumber of parameters: \t pi: %d, \t' + \
'q1: %d, \t q2: %d, \t v: %d, \t total: %d\n')%var_counts)
print(mu.shape, pi.shape, logp_pi.shape, q1.shape, q2.shape, q1_pi.shape,
q2_pi.shape, v.shape,
tf.expand_dims(d_ph, 1).shape,
tf.expand_dims(d_ph, 1).shape, v_targ.shape)
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
# Targets for Q and V regression
q_backup = tf.stop_gradient(
tf.expand_dims(r_ph, 1) + gamma *
(1 - tf.expand_dims(d_ph, 1)) * v_targ)
# q_backup = tf.stop_gradient(r_ph + gamma*(1-d_ph))
v_backup = tf.stop_gradient(min_q_pi - alpha * logp_pi)
# Soft actor-critic losses
pi_loss = tf.reduce_mean(alpha * logp_pi - q1_pi)
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2)**2)
v_loss = 0.5 * tf.reduce_mean((v_backup - v)**2)
value_loss = q1_loss + q2_loss + v_loss
# Policy train op
# (has to be separate from value train op, because q1_pi appears in pi_loss)
pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
# Value train op
# (control dep of train_pi_op because sess.run otherwise evaluates in nondeterministic order)
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
value_params = get_vars('main/q') + get_vars('main/v')
with tf.control_dependencies([train_pi_op]):
train_value_op = value_optimizer.minimize(value_loss,
var_list=value_params)
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# All ops to call during one training step
step_ops = [
pi_loss, q1_loss, q2_loss, v_loss, q1, q2, v, logp_pi, train_pi_op,
train_value_op, target_update
]
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph
},
outputs={
'mu': mu,
'pi': pi,
'q1': q1,
'q2': q2,
'v': v
})
def get_action(o, head, deterministic=False):
# act_op = mu[:,p_head,:] if deterministic else pi[:,p_head,:]
act_op = mu if deterministic else pi
return sess.run(act_op, feed_dict={x_ph: o.reshape(1, -1)})[0, head, :]
def test_agent(n, head):
global sess, mu, pi, q1, q2, q1_pi, q2_pi
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
# head = np.random.randint(num_heads, size = 1)[0]
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
o, r, d, _ = test_env.step(get_action(o, head, True))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
policy = LM_DSEE(ac_kwargs['num_heads'],
rho=0.49,
lower=-50,
amplitude=4450)
policy.startGame()
returns = []
choices = []
head = policy.choice()
# print ('Total number of heads', ac_kwargs['num_heads'])
# Main loop: collect experience in env and update/log each epoch
train_end = start_time
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > start_steps:
a = get_action(o, head)
else:
a = env.action_space.sample()
# a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
train_start = time.time()
# print (t//steps_per_epoch, "Playing time", train_start - train_end)
policy.getReward(head, ep_ret)
returns.append(ep_ret)
choices.append(head)
head = policy.choice()
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
# tic = time.time()
outs = sess.run(step_ops, feed_dict)
# toc = time.time()
# print (toc-tic)
logger.store(LossPi=outs[0],
LossQ1=outs[1],
LossQ2=outs[2],
LossV=outs[3],
Q1Vals=outs[4],
Q2Vals=outs[5],
VVals=outs[6],
LogPi=outs[7])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
train_end = time.time()
# print (t//steps_per_epoch, "Training time", train_end - train_start)
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
test_start = time.time()
epoch = t // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
head = policy.choice()
test_agent(n=10, head=head)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
test_end = time.time()
# print (t//steps_per_epoch, "Testing time", test_end - test_start)
# print ("*"*30)
print(returns, choices)
def ddpg(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
pi_lr=1e-3,
q_lr=1e-3,
batch_size=100,
start_steps=10000,
update_after=1000,
update_every=50,
act_noise=0.1,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1):
"""
Deep Deterministic Policy Gradient (DDPG)
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: The constructor method for a PyTorch Module with an ``act``
method, a ``pi`` module, and a ``q`` module. The ``act`` method and
``pi`` module should accept batches of observations as inputs,
and ``q`` should accept a batch of observations and a batch of
actions as inputs. When called, these should return:
=========== ================ ======================================
Call Output Shape Description
=========== ================ ======================================
``act`` (batch, act_dim) | Numpy array of actions for each
| observation.
``pi`` (batch, act_dim) | Tensor containing actions from policy
| given observations.
``q`` (batch,) | Tensor containing the current estimate
| of Q* for the provided observations
| and actions. (Critical: make sure to
| flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
you provided to DDPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
pi_lr (float): Learning rate for policy.
q_lr (float): Learning rate for Q-networks.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
update_after (int): Number of env interactions to collect before
starting to do gradient descent updates. Ensures replay buffer
is full enough for useful updates.
update_every (int): Number of env interactions that should elapse
between gradient descent updates. Note: Regardless of how long
you wait between updates, the ratio of env steps to gradient steps
is locked to 1.
act_noise (float): Stddev for Gaussian exploration noise added to
policy at training time. (At test time, no noise is added.)
num_test_episodes (int): Number of episodes to test the deterministic
policy at the end of each epoch.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Create actor-critic module and target networks
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
ac_targ = deepcopy(ac)
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in ac_targ.parameters():
p.requires_grad = False
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables (protip: try to get a feel for how different size networks behave!)
var_counts = tuple(core.count_vars(module) for module in [ac.pi, ac.q])
logger.log('\nNumber of parameters: \t pi: %d, \t q: %d\n' % var_counts)
# Set up function for computing DDPG Q-loss
def compute_loss_q(data):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
q = ac.q(o, a)
assert len(q.shape) == 1 and q.shape[
0] == batch_size, 'Expected shape (%d,), got %s' % (batch_size,
str(q.shape))
# Bellman backup for Q function
with torch.no_grad():
q_pi_targ = ac_targ.q(o2, ac_targ.pi(o2))
backup = r + gamma * (1 - d) * q_pi_targ
# MSE loss against Bellman backup
loss_q = ((q - backup)**2).mean()
# Useful info for logging
loss_info = dict(QVals=q.detach().numpy())
return loss_q, loss_info
# Set up function for computing DDPG pi loss
def compute_loss_pi(data):
o = data['obs']
q_pi = ac.q(o, ac.pi(o))
return -q_pi.mean()
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
q_optimizer = Adam(ac.q.parameters(), lr=q_lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data):
# First run one gradient descent step for Q.
q_optimizer.zero_grad()
loss_q, loss_info = compute_loss_q(data)
loss_q.backward()
q_optimizer.step()
# Freeze Q-network so you don't waste computational effort
# computing gradients for it during the policy learning step.
for p in ac.q.parameters():
p.requires_grad = False
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi = compute_loss_pi(data)
loss_pi.backward()
pi_optimizer.step()
# Unfreeze Q-network so you can optimize it at next DDPG step.
for p in ac.q.parameters():
p.requires_grad = True
# Record things
logger.store(LossQ=loss_q.item(), LossPi=loss_pi.item(), **loss_info)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(ac.parameters(), ac_targ.parameters()):
# NB: We use an in-place operations "mul_", "add_" to update target
# params, as opposed to "mul" and "add", which would make new tensors.
p_targ.data.mul_(polyak)
p_targ.data.add_((1 - polyak) * p.data)
def get_action(o, noise_scale):
a = ac.act(torch.as_tensor(o, dtype=torch.float32))
a += noise_scale * np.random.randn(act_dim)
return np.clip(a, -act_limit, act_limit)
def test_agent():
for j in range(num_test_episodes):
o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, _ = test_env.step(get_action(o, 0))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
# Prepare for interaction with environment
total_steps = steps_per_epoch * epochs
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy (with some noise, via act_noise).
if t > start_steps:
a = get_action(o, act_noise)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
# End of trajectory handling
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Update handling
if t >= update_after and t % update_every == 0:
for _ in range(update_every):
batch = replay_buffer.sample_batch(batch_size)
update(data=batch)
# End of epoch handling
if (t + 1) % steps_per_epoch == 0:
epoch = (t + 1) // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('QVals', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sad(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=1e-3,
alpha=0.2,
batch_size=100,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=100,
demo_file=''):
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Create actor-critic module and target networks
ac = actor_critic(env.observation_space, env.action_space,
**ac_kwargs).to(device)
ac_targ = deepcopy(ac).to(device)
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in ac_targ.parameters():
p.requires_grad = False
# List of parameters for both Q-networks (save this for convenience)
q_params = itertools.chain(ac.q1.parameters(), ac.q2.parameters())
# Expert replay buffer
demo_buffer = DemoBuffer()
demo_buffer.load(demo_file)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables (protip: try to get a feel for how different size networks behave!)
var_counts = tuple(
core.count_vars(module) for module in [ac.pi, ac.q1, ac.q2])
logger.log('\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d\n' %
var_counts)
# Set up function for computing SAC Q-losses
def compute_loss_q(data):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
o, a, r, o2, d = Variable(o), Variable(a), Variable(r), Variable(
o2), Variable(d)
q1 = ac.q1(o, a)
q2 = ac.q2(o, a)
# Bellman backup for Q functions
with torch.no_grad():
# Target actions come from *current* policy
a2, logp_a2 = ac.pi(o2)
# Target Q-values
q1_pi_targ = ac_targ.q1(o2, a2)
q2_pi_targ = ac_targ.q2(o2, a2)
q_pi_targ = torch.min(q1_pi_targ, q2_pi_targ)
backup = r + gamma * (1 - d) * (q_pi_targ - alpha * logp_a2)
# MSE loss against Bellman backup
loss_q1 = ((q1 - backup)**2).mean()
loss_q2 = ((q2 - backup)**2).mean()
loss_q = loss_q1 + loss_q2
# Useful info for logging
q_info = dict(Q1Vals=q1.cpu().detach().numpy(),
Q2Vals=q2.cpu().detach().numpy())
return loss_q, q_info
# Set up function for computing SAC pi loss
def compute_loss_pi(data):
o = data['obs']
o = Variable(o)
pi, logp_pi = ac.pi(o)
q1_pi = ac.q1(o, pi)
q2_pi = ac.q2(o, pi)
q_pi = torch.min(q1_pi, q2_pi)
# Entropy-regularized policy loss
loss_pi = (alpha * logp_pi - q_pi).mean()
# Useful info for logging
pi_info = dict(LogPi=logp_pi.cpu().detach().numpy())
return loss_pi, pi_info
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=lr)
q_optimizer = Adam(q_params, lr=lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data):
# First run one gradient descent step for Q1 and Q2
q_optimizer.zero_grad()
loss_q, q_info = compute_loss_q(data)
loss_q.backward()
q_optimizer.step()
# Record things
logger.store(LossQ=loss_q.item(), **q_info)
# Freeze Q-networks so you don't waste computational effort
# computing gradients for them during the policy learning step.
for p in q_params:
p.requires_grad = False
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
loss_pi.backward()
pi_optimizer.step()
# Unfreeze Q-networks so you can optimize it at next DDPG step.
for p in q_params:
p.requires_grad = True
# Record things
logger.store(LossPi=loss_pi.item(), **pi_info)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(ac.parameters(), ac_targ.parameters()):
# NB: We use an in-place operations "mul_", "add_" to update target
# params, as opposed to "mul" and "add", which would make new tensors.
p_targ.data.mul_(polyak)
p_targ.data.add_((1 - polyak) * p.data)
# all_obs, all_action = torch.tensor(demo_buffer.obs_buf, device=device), demo_buffer.act_buf
# stack = []
# global counter
# counter = 0
def get_action(o, deterministic=False):
o = torch.as_tensor(o, device=device, dtype=torch.float32)
# norm = torch.norm(all_obs - o, dim=1)
# idx_min = torch.argmin(norm)
# if norm[idx_min] < 0.3:
# action = all_action[idx_min.item()]
# stack.append(1)
# else:
action = ac.act(o, deterministic).cpu().numpy()
return action
def test_agent():
for j in range(num_test_episodes):
o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
o, r, d, _ = test_env.step(get_action(o, True))
ep_ret += r
ep_len += 1
writer.add_scalar(tag='test_reward',
scalar_value=ep_ret,
global_step=t)
test_reward_buffer.append((t, ep_ret))
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
# Prepare for interaction with environment
total_steps = steps_per_epoch * epochs
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
for i in range(update_every * 10):
batch = demo_buffer.sample_batch(batch_size)
update(batch)
# if i % update_every == 1:
# logger.log_tabular('LossQ', average_only=True)
# logger.dump_tabular()
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
if t > start_steps:
a = get_action(o)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
# End of trajectory handling
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
writer.add_scalar(tag='train_reward',
scalar_value=ep_ret,
global_step=t)
train_reward_buffer.append((t, ep_ret))
o, ep_ret, ep_len = env.reset(), 0, 0
# Update handling
if t >= update_after and t % update_every == 0:
for j in range(update_every):
batch = replay_buffer.sample_batch(batch_size)
# batch = core.merge_batch(batch1, batch2)
update(data=batch)
# End of epoch handling
if (t + 1) % steps_per_epoch == 0:
epoch = (t + 1) // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs):
logger.save_state({'env': env}, None)
output_dir = logger_kwargs['output_dir'] + '/'
test_rewards = np.array(test_reward_buffer)
train_rewards = np.array(train_reward_buffer)
train_file_name = os.path.join(
output_dir, '{}_train_rewards.npy'.format(seed))
test_file_name = os.path.join(
output_dir, '{}_test_rewards.npy'.format(seed))
np.save(train_file_name, train_rewards)
np.save(test_file_name, test_rewards)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
# logger.log_tabular('Demo action', len(stack))
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
writer.close()
def vpg(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
pi_lr=3e-4,
vf_lr=1e-3,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=10,
n_test_episodes=100):
"""
Vanilla Policy Gradient
(with GAE-Lambda for advantage estimation)
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: The constructor method for a PyTorch Module with a
``step`` method, an ``act`` method, a ``pi`` module, and a ``v``
module. The ``step`` method should accept a batch of observations
and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``a`` (batch, act_dim) | Numpy array of actions for each
| observation.
``v`` (batch,) | Numpy array of value estimates
| for the provided observations.
``logp_a`` (batch,) | Numpy array of log probs for the
| actions in ``a``.
=========== ================ ======================================
The ``act`` method behaves the same as ``step`` but only returns ``a``.
The ``pi`` module's forward call should accept a batch of
observations and optionally a batch of actions, and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` N/A | Torch Distribution object, containing
| a batch of distributions describing
| the policy for the provided observations.
``logp_a`` (batch,) | Optional (only returned if batch of
| actions is given). Tensor containing
| the log probability, according to
| the policy, of the provided actions.
| If actions not given, will contain
| ``None``.
=========== ================ ======================================
The ``v`` module's forward call should accept a batch of observations
and return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``v`` (batch,) | Tensor containing the value estimates
| for the provided observations. (Critical:
| make sure to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
you provided to VPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
n_test_episodes (int): Number of episodes for test agent evaluation at
the end of each epoch.
"""
# Special function to avoid certain slowdowns from PyTorch + MPI combo.
#setup_pytorch_for_mpi()
# Set up logger and save configuration
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Random seed
seed += 10000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
# Instantiate environment
env = env_fn()
def space_dim(space):
if isinstance(space, Box):
return space.shape
elif isinstance(space, Discrete):
return space.n
else:
raise ValueError
obs_dim = space_dim(env.observation_space)
act_dim = space_dim(env.action_space)
# Create actor-critic module
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
# if torch.cuda.device_count() > 1:
# ac.pi = nn.DataParallel(ac.pi)
# ac.v = nn.DataParallel(ac.v)
ac.to(device)
# Sync params across processes
#sync_params(ac)
# Count variables
var_counts = tuple(core.count_vars(module) for module in [ac.pi, ac.v])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# Set up experience buffer
local_steps_per_epoch = steps_per_epoch
#local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = VPGBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Set up function for computing VPG policy loss
def compute_loss_pi(data):
obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data[
'logp']
# Policy loss
pi, logp = ac.pi(obs, act)
loss_pi = -(logp * adv).mean()
# Useful extra info
approx_kl = (logp_old - logp).mean().item()
ent = pi.entropy().mean().item()
pi_info = dict(kl=approx_kl, ent=ent)
return loss_pi, pi_info
# Set up function for computing value loss
def compute_loss_v(data):
obs, ret = data['obs'], data['ret']
return ((ac.v(obs) - ret)**2).mean()
# Set up optimizers for policy and value function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update():
data = buf.get()
# Get loss and info values before update
pi_l_old, pi_info_old = compute_loss_pi(data)
pi_l_old = pi_l_old.item()
v_l_old = compute_loss_v(data).item()
# Train policy with a single step of gradient descent
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
loss_pi.backward()
#mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
# Value function learning
for i in range(train_v_iters):
vf_optimizer.zero_grad()
loss_v = compute_loss_v(data)
loss_v.backward()
#mpi_avg_grads(ac.v) # average grads across MPI processes
vf_optimizer.step()
# Log changes from update
kl, ent = pi_info['kl'], pi_info_old['ent']
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
DeltaLossPi=(loss_pi.item() - pi_l_old),
DeltaLossV=(loss_v.item() - v_l_old))
def test_agent():
test_env = env_fn()
o, test_ep_ret, test_ep_len = test_env.reset(), 0, 0
num_episodes = 0
while num_episodes < n_test_episodes:
a, _, _ = ac.step(
torch.as_tensor(o, dtype=torch.float32, device=device))
o2, r, d, _ = env.step(a)
test_ep_ret += r
test_ep_len += 1
o = o2
timeout = ep_len == max_ep_len
terminal = d or timeout
if timeout or terminal:
logger.store(TestEpRet=test_ep_ret)
num_episodes += 1
o, test_ep_ret, test_ep_len = test_env.reset(), 0, 0
# Prepare for interaction with environment
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v, logp = ac.step(
torch.as_tensor(o, dtype=torch.float32, device=device))
next_o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# save and log
buf.store(o, a, r, v, logp)
logger.store(VVals=v)
# Update obs (critical!)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len,
flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
_, v, _ = ac.step(
torch.as_tensor(o, dtype=torch.float32, device=device))
else:
v = 0
buf.finish_path(v)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform VPG update!
update()
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
wandb.log(logger.log_current_row, step=epoch)
logger.dump_tabular()
def ppo(workload_file,
model_path,
ac_kwargs=dict(),
seed=0,
traj_per_epoch=4000,
epochs=50,
gamma=0.99,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=1e-3,
train_pi_iters=80,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10,
pre_trained=0,
trained_model=None,
attn=False,
shuffle=False,
backfil=False,
skip=False,
score_type=0,
batch_job_slice=0,
sched_algo=4):
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env = HPCEnvSkip(shuffle=shuffle,
backfil=backfil,
skip=skip,
job_score_type=score_type,
batch_job_slice=batch_job_slice,
build_sjf=False,
sched_algo=sched_algo)
env.seed(seed)
env.my_init(workload_file=workload_file, sched_file=model_path)
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
ac_kwargs['attn'] = attn
# Inputs to computation graph
buf = PPOBuffer(obs_dim, act_dim, traj_per_epoch * JOB_SEQUENCE_SIZE,
gamma, lam)
if pre_trained:
sess = tf.Session()
model = restore_tf_graph(sess, trained_model)
logger.log('load pre-trained model')
# Count variables
var_counts = tuple(count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' %
var_counts)
x_ph = model['x']
a_ph = model['a']
mask_ph = model['mask']
adv_ph = model['adv']
ret_ph = model['ret']
logp_old_ph = model['logp_old_ph']
pi = model['pi']
v = model['v']
# logits = model['logits']
out = model['out']
logp = model['logp']
logp_pi = model['logp_pi']
pi_loss = model['pi_loss']
v_loss = model['v_loss']
approx_ent = model['approx_ent']
approx_kl = model['approx_kl']
clipfrac = model['clipfrac']
clipped = model['clipped']
# Optimizers
# graph = tf.get_default_graph()
# op = sess.graph.get_operations()
# [print(m.values()) for m in op]
# train_pi = graph.get_tensor_by_name('pi/conv2d/kernel/Adam:0')
# train_v = graph.get_tensor_by_name('v/conv2d/kernel/Adam:0')
train_pi = tf.get_collection("train_pi")[0]
train_v = tf.get_collection("train_v")[0]
# train_pi_optimizer = MpiAdamOptimizer(learning_rate=pi_lr, name='AdamLoad')
# train_pi = train_pi_optimizer.minimize(pi_loss)
# train_v_optimizer = MpiAdamOptimizer(learning_rate=vf_lr, name='AdamLoad')
# train_v = train_v_optimizer.minimize(v_loss)
# sess.run(tf.variables_initializer(train_pi_optimizer.variables()))
# sess.run(tf.variables_initializer(train_v_optimizer.variables()))
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, mask_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi, out]
else:
x_ph, a_ph = placeholders_from_spaces(env.observation_space,
env.action_space)
# y_ph = placeholder(JOB_SEQUENCE_SIZE*3) # 3 is the number of sequence features
mask_ph = placeholder(env.action_space.n)
adv_ph, ret_ph, logp_old_ph = placeholders(None, None, None)
# Main outputs from computation graph
pi, logp, logp_pi, v, out = actor_critic(x_ph, a_ph, mask_ph,
**ac_kwargs)
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, mask_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi, out]
# Experience buffer
# Count variables
var_counts = tuple(count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' %
var_counts)
# PPO objectives
ratio = tf.exp(logp - logp_old_ph) # pi(a|s) / pi_old(a|s)
min_adv = tf.where(adv_ph > 0, (1 + clip_ratio) * adv_ph,
(1 - clip_ratio) * adv_ph)
pi_loss = -tf.reduce_mean(tf.minimum(ratio * adv_ph, min_adv))
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Info (useful to watch during learning)
approx_kl = tf.reduce_mean(
logp_old_ph -
logp) # a sample estimate for KL-divergence, easy to compute
approx_ent = tf.reduce_mean(
-logp) # a sample estimate for entropy, also easy to compute
clipped = tf.logical_or(ratio > (1 + clip_ratio), ratio <
(1 - clip_ratio))
clipfrac = tf.reduce_mean(tf.cast(clipped, tf.float32))
# Optimizers
train_pi = tf.train.AdamOptimizer(
learning_rate=pi_lr).minimize(pi_loss)
train_v = tf.train.AdamOptimizer(learning_rate=vf_lr).minimize(v_loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
tf.add_to_collection("train_pi", train_pi)
tf.add_to_collection("train_v", train_v)
# Setup model saving
# logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'action_probs': action_probs, 'log_picked_action_prob': log_picked_action_prob, 'v': v})
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph,
'adv': adv_ph,
'mask': mask_ph,
'ret': ret_ph,
'logp_old_ph': logp_old_ph
},
outputs={
'pi': pi,
'v': v,
'out': out,
'pi_loss': pi_loss,
'logp': logp,
'logp_pi': logp_pi,
'v_loss': v_loss,
'approx_ent': approx_ent,
'approx_kl': approx_kl,
'clipped': clipped,
'clipfrac': clipfrac
})
def update():
inputs = {k: v for k, v in zip(all_phs, buf.get())}
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Training
for i in range(train_pi_iters):
_, kl = sess.run([train_pi, approx_kl], feed_dict=inputs)
kl = mpi_avg(kl)
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
logger.store(StopIter=i)
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
pi_l_new, v_l_new, kl, cf = sess.run(
[pi_loss, v_loss, approx_kl, clipfrac], feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
start_time = time.time()
[o, co], r, d, ep_ret, ep_len, show_ret, sjf, f1, skip_count = env.reset(
), 0, False, 0, 0, 0, 0, 0, 0
# Main loop: collect experience in env and update/log each epoch
start_time = time.time()
for epoch in range(epochs):
t = 0
discard_times = 0
while True:
# [no_skip, skip]
lst = [1, 1]
#for i in range(0, MAX_QUEUE_SIZE * JOB_FEATURES, JOB_FEATURES):
# job = o[i:i + JOB_FEATURES]
# # the skip time of will_skip job exceeds MAX_SKIP_TIME
# if job[-2] == 1.0:
# lst = [1,0]
a, v_t, logp_t, output = sess.run(get_action_ops,
feed_dict={
x_ph:
o.reshape(1, -1),
mask_ph:
np.array(lst).reshape(1, -1)
})
# print(a, end=" ")
'''
action = np.random.choice(np.arange(MAX_QUEUE_SIZE), p=action_probs)
log_action_prob = np.log(action_probs[action])
'''
if buf.ptr - buf.path_start_idx >= 10 * JOB_SEQUENCE_SIZE or buf.ptr >= buf.max_size:
discard_times += 1
buf.ptr = buf.path_start_idx
[
o, co
], r, d, ep_ret, ep_len, show_ret, sjf, f1, skip_count = env.reset(
), 0, False, 0, 0, 0, 0, 0, 0
continue
# save and log
buf.store(o, None, a, np.array(lst), r, v_t, logp_t)
logger.store(VVals=v_t)
if a[0] == 1:
skip_count += 1
o, r, d, r2, sjf_t, f1_t = env.step(a[0])
ep_ret += r
ep_len += 1
show_ret += r2
sjf += sjf_t
f1 += f1_t
if d:
t += 1
buf.finish_path(r)
logger.store(EpRet=ep_ret,
EpLen=ep_len,
ShowRet=show_ret,
SJF=sjf,
F1=f1,
SkipRatio=skip_count / ep_len)
[
o, co
], r, d, ep_ret, ep_len, show_ret, sjf, f1, skip_count = env.reset(
), 0, False, 0, 0, 0, 0, 0, 0
if t >= traj_per_epoch:
# print ("state:", state, "\nlast action in a traj: action_probs:\n", action_probs, "\naction:", action)
break
# print("Sample time:", (time.time()-start_time)/num_total, num_total)
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform PPO update!
# start_time = time.time()
update()
# print("Train time:", time.time()-start_time)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', with_min_and_max=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts',
(epoch + 1) * traj_per_epoch * JOB_SEQUENCE_SIZE)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('ShowRet', average_only=True)
logger.log_tabular('SJF', average_only=True)
logger.log_tabular('F1', average_only=True)
logger.log_tabular('SkipRatio', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def ude_td3_ConcreteD_batchP(
env_fn,
render_env=False,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=5000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
pi_lr=1e-3,
q_lr=1e-3,
reward_scale=5,
without_start_steps=True,
batch_size=100,
# TODO: change it back to 10000
start_steps=10000, #start_steps=10000,
without_delay_train=False,
act_noise=0.1,
target_noise=0.2,
noise_clip=0.5,
policy_delay=2,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1,
n_post_action=10,
uncertainty_method='dropout',
sample_obs_std=1,
uncertainty_driven_exploration=False,
uncertainty_policy_delay=5000,
dropout_rate=0.1,
concentration_factor=0.1,
minimum_exploration_level=0):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Deterministically computes actions
| from policy given states.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to TD3.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
pi_lr (float): Learning rate for policy.
q_lr (float): Learning rate for Q-networks.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
act_noise (float): Stddev for Gaussian exploration noise added to
policy at training time. (At test time, no noise is added.)
target_noise (float): Stddev for smoothing noise added to target
policy.
noise_clip (float): Limit for absolute value of target policy
smoothing noise.
policy_delay (int): Policy will only be updated once every
policy_delay times for each update of the Q-networks.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
# TODO: Test no start steps
if without_start_steps:
start_steps = batch_size
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim,
obs_dim, None, None)
print('Creating networks ...')
# Main outputs from computation graph
with tf.variable_scope('main'):
pi, _, pi_dropout_mask_generator, pi_dropout_mask_phs, \
q1, _, q1_dropout_mask_generator, q1_dropout_mask_phs, q1_pi, _, \
q2, _, q2_dropout_mask_generator, q2_dropout_mask_phs = actor_critic(x_ph, a_ph, **ac_kwargs,
dropout_rate=0)
# Random Network Distillation
with tf.variable_scope('random_net_distill'):
# RND Target and Predictor Network
rnd_lr = 1e-3
rnd_targ_act, \
rnd_pred_act, rnd_pred_act_reg, rnd_pred_act_dropout_mask_generator, rnd_pred_act_dropout_mask_phs, \
rnd_targ_cri, \
rnd_pred_cri, rnd_pred_cri_reg, rnd_pred_cri_dropout_mask_generator, rnd_pred_cri_dropout_mask_phs = core.random_net_distill(x_ph, a_ph, **ac_kwargs, dropout_rate=0)
# TODO: add environment model learning transition dynamics
# TODO: Calculate Uncertainty of Q-value function
# Initialize uncertainty module
obs_set_size = 10
track_obs_set_unc_frequency = 100 # every 100 steps
pi_unc_module = DropoutUncertaintyModule(
act_dim,
obs_dim,
n_post_action,
obs_set_size,
track_obs_set_unc_frequency,
x_ph,
a_ph,
ac_kwargs,
dropout_rate,
logger_kwargs,
tf_var_scope_main='main',
tf_var_scope_target='target',
tf_var_scope_rnd='random_net_distill')
# Target policy network
with tf.variable_scope('target'):
pi_targ, _, pi_dropout_mask_generator_targ, pi_dropout_mask_phs_targ, \
_, _, _, _, _, _, \
_, _, _, _ = actor_critic(x2_ph, a_ph, **ac_kwargs, dropout_rate=dropout_rate)
pi_targ = pi_targ[0]
# Target Q networks
with tf.variable_scope('target', reuse=True):
# TODO: add with_out_policy_smoothing
# Target policy smoothing, by adding clipped noise to target actions
epsilon = tf.random_normal(tf.shape(pi_targ), stddev=target_noise)
epsilon = tf.clip_by_value(epsilon, -noise_clip, noise_clip)
a2 = pi_targ + epsilon
a2 = tf.clip_by_value(a2, -act_limit, act_limit)
# Target Q-values, using action from target policy
_, _, _, _, \
q1_targ, _, q1_dropout_mask_generator_targ, q1_dropout_mask_phs_targ, _, _, \
q2_targ, _, q2_dropout_mask_generator_targ, q2_dropout_mask_phs_targ = actor_critic(x2_ph, a2, **ac_kwargs, dropout_rate=dropout_rate)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size,
logger_fname='experiences_log.txt',
**logger_kwargs)
# Count variables
var_counts = tuple(
core.count_vars(scope)
for scope in ['main/pi', 'main/q1', 'main/q2', 'main'])
print(
'\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d, \t total: %d\n'
% var_counts)
# TODO: use conservative estimation of Q
# Bellman backup for Q functions, using Clipped Double-Q targets
def post_sample_q1_and_q2(feed_dictionary, batch_size):
dropout_masks_set_q1 = q1_dropout_mask_generator_targ.generate_dropout_mask(
n_post_action)
dropout_masks_set_q2 = q2_dropout_mask_generator_targ.generate_dropout_mask(
n_post_action)
q1_targ_post = np.zeros((n_post_action, batch_size))
q2_targ_post = np.zeros((n_post_action, batch_size))
for mask_i in range(len(q1_dropout_mask_phs_targ)):
feed_dictionary[q1_dropout_mask_phs_targ[
mask_i]] = dropout_masks_set_q1[mask_i]
feed_dictionary[q2_dropout_mask_phs_targ[
mask_i]] = dropout_masks_set_q2[mask_i]
q1_targ_post = sess.run(q1_targ, feed_dict=feed_dictionary)
q2_targ_post = sess.run(q2_targ, feed_dict=feed_dictionary)
min_q_targ = np.minimum(q1_targ_post.mean(axis=1),
q2_targ_post.mean(axis=1))
return min_q_targ
# min_q_targ = tf.placeholder(dtype=tf.float32)
# backup = tf.stop_gradient(r_ph + gamma*(1-d_ph)*min_q_targ)
min_q_targ = tf.minimum(q1_targ[0], q2_targ[0])
backup = tf.stop_gradient(r_ph + gamma * (1 - d_ph) * min_q_targ)
# TD3 losses
pi_loss = -tf.reduce_mean(q1_pi[0])
q1_loss = tf.reduce_mean((q1[0] - backup)**2)
q2_loss = tf.reduce_mean((q2[0] - backup)**2)
q_loss = q1_loss + q2_loss
# Separate train ops for pi, q
pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr)
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
train_q_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q'))
# RND losses and train ops
rnd_loss_act = tf.reduce_mean(
(rnd_pred_act[0] - rnd_targ_act)**2) + rnd_pred_act_reg / batch_size
rnd_optimizer_act = tf.train.AdamOptimizer(learning_rate=rnd_lr)
train_rnd_op_act = rnd_optimizer_act.minimize(
rnd_loss_act, var_list=get_vars('random_net_distill/rnd_pred_act'))
rnd_loss_cri = tf.reduce_mean(
(rnd_pred_cri[0] - rnd_targ_cri)**2) + rnd_pred_cri_reg / batch_size
rnd_optimizer_cri = tf.train.AdamOptimizer(learning_rate=rnd_lr)
train_rnd_op_cri = rnd_optimizer_cri.minimize(
rnd_loss_cri, var_list=get_vars('random_net_distill/rnd_pred_cri'))
# Polyak averaging for target variables
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph
},
outputs={
'pi': pi,
'q1': q1,
'q2': q2
})
def set_dropout_mask_and_post_number_to_one(feed_dictionary, *argv):
"""Set all dropout masks and post sample number in argv to one."""
for dropout_mask_ph in argv:
for mask_i in range(len(dropout_mask_ph)):
feed_dictionary[dropout_mask_ph[mask_i]] = np.ones(
[1, dropout_mask_ph[mask_i].shape.as_list()[1]])
return feed_dictionary
def set_dropout_mask_randomly_and_post_nuber_to_one(
feed_dictionary, mask_phs, mask_generators):
if len(mask_phs) != len(mask_generators):
raise ValueError('mask_phs and mask_generators do not match.')
else:
for i in range(len(mask_phs)):
dropout_mask_ph = mask_phs[i]
dropout_masks = mask_generators[i].generate_dropout_mask(
post_size=1)
for mask_i in range(len(dropout_mask_ph)):
feed_dictionary[
dropout_mask_ph[mask_i]] = dropout_masks[mask_i]
return feed_dictionary
def get_action_train(o, noise_scale, pi_unc_module, step_index):
feed_dictionary = {x_ph: o.reshape(1, -1)}
# Set dropout masks to one
feed_dictionary = set_dropout_mask_and_post_number_to_one(
feed_dictionary, rnd_pred_act_dropout_mask_phs,
rnd_pred_cri_dropout_mask_phs, pi_dropout_mask_phs,
q1_dropout_mask_phs, q2_dropout_mask_phs)
# RND actor
rnd_t_act, rnd_p_act = sess.run([rnd_targ_act, rnd_pred_act],
feed_dict=feed_dictionary)
rnd_t_act = rnd_t_act[0]
rnd_p_act = rnd_p_act[0]
rnd_e_act = np.sqrt(np.sum(rnd_p_act - rnd_t_act)**2)
# Generate action
if uncertainty_driven_exploration:
# 1. Generate action Prediction
a_prediction = sess.run(pi, feed_dict=feed_dictionary)[0][0]
# 2. Generate post sampled actions
# TODO： get covariance based on online and target policy respectively and calculate the difference
a_post = pi_unc_module.get_post_samples_act(o, sess, step_index)
rnd_a_post = pi_unc_module.get_post_samples_rnd_act(
o, sess, step_index)
# 3. Generate uncertainty-driven exploratory action
a = np.zeros((act_dim, ))
if act_dim > 1:
# TODO: compute correlation rather than covariance
a_cov = np.cov(a_post, rowvar=False)
a_cov_shaped = concentration_factor * a_cov
rnd_a_cov = np.cov(rnd_a_post, rowvar=False)
a = np.random.multivariate_normal(a_prediction, a_cov_shaped,
1)[0]
unc_a = a_cov
unc_rnd_a = rnd_a_cov
else:
a_std = np.std(a_post, axis=0)
a_std_shaped = concentration_factor * a_std + minimum_exploration_level * np.ones(
a_std.shape)
rnd_a_cov = np.std(rnd_a_post, axis=0)
# TODO: only keep one
a = np.random.normal(a_prediction, a_std_shaped, 1)[0]
unc_a = a_std
unc_rnd_a = rnd_a_cov
else:
for mask_i in range(len(pi_dropout_mask_phs)):
feed_dictionary[pi_dropout_mask_phs[mask_i]] = np.ones(
[1, pi_dropout_mask_phs[mask_i].shape.as_list()[1]])
a = sess.run(pi, feed_dict=feed_dictionary)[0][0]
a += noise_scale * np.random.randn(act_dim)
unc_a = 0
unc_rnd_a = 0
a = np.clip(a, -act_limit, act_limit)
# TODO: use uncertainty as intrinsic reward
unc_based_reward = np.mean(np.abs(unc_a))
# TODO: should the a_ph be a or a_prediction??
feed_dictionary[a_ph] = a.reshape(1, -1)
# Generate post sampled q values
q1_post, q2_post = pi_unc_module.get_post_samples_q(
o, a, sess, step_index)
rnd_q_post = pi_unc_module.get_post_samples_rnd_cri(
o, a, sess, step_index)
unc_q1 = np.std(q1_post, axis=0)
unc_q2 = np.std(q2_post, axis=0)
unc_rnd_q = np.std(rnd_q_post, axis=0)
q1_pred = sess.run(q1, feed_dict=feed_dictionary)[0][0]
q2_pred = sess.run(q2, feed_dict=feed_dictionary)[0][0]
# RND critic
rnd_t_cri, rnd_p_cri = sess.run([rnd_targ_cri, rnd_pred_cri],
feed_dict=feed_dictionary)
rnd_t_cri = rnd_t_cri[0]
rnd_p_cri = rnd_p_cri[0]
rnd_e_cri = np.sqrt(np.sum(rnd_p_cri - rnd_t_cri)**2)
return a, \
q1_pred, q2_pred, q1_post, q2_post,\
unc_a, unc_based_reward, unc_q1, unc_q2,\
unc_rnd_a, unc_rnd_q,\
rnd_t_act, rnd_p_act, rnd_e_act,\
rnd_t_cri, rnd_p_cri, rnd_e_cri
def get_action_test(o):
"""Get deterministic action without exploration."""
feed_dictionary = {x_ph: o.reshape(1, -1)}
for mask_i in range(len(pi_dropout_mask_phs)):
feed_dictionary[pi_dropout_mask_phs[mask_i]] = np.ones(
[1, pi_dropout_mask_phs[mask_i].shape.as_list()[1]])
a = sess.run(pi, feed_dict=feed_dictionary)[0][0]
return np.clip(a, -act_limit, act_limit)
def test_agent(n=10):
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, _ = test_env.step(get_action_test(o))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
ep_q1_var, ep_q2_var, ep_unc_a, ep_unc_q1, ep_unc_q2, \
ep_unc_rnd_a, ep_unc_rnd_q, ep_rnd_e_act, ep_rnd_e_cri = 0, 0, 0, 0, 0, 0, 0, 0, 0
total_steps = steps_per_epoch * epochs
# No dropout and no post sample for training phase: set all dropout masks to 1 and post_size to 1
feed_dict_train = {}
feed_dict_train = set_dropout_mask_and_post_number_to_one(
feed_dict_train, pi_dropout_mask_phs, q1_dropout_mask_phs,
q2_dropout_mask_phs, pi_dropout_mask_phs_targ,
q1_dropout_mask_phs_targ, q2_dropout_mask_phs_targ,
rnd_pred_act_dropout_mask_phs, rnd_pred_cri_dropout_mask_phs)
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy (with some noise, via act_noise).
"""
if t > start_steps:
# import pdb; pdb.set_trace()
a, \
q1_pred, q2_pred, q1_post, q2_post,\
unc_a, unc_based_reward, unc_q1, unc_q2, \
unc_rnd_a, unc_rnd_q, \
rnd_t_act, rnd_p_act, rnd_e_act,\
rnd_t_cri, rnd_p_cri, rnd_e_cri = get_action_train(o, act_noise, pi_unc_module, step_index=t)
else:
a = env.action_space.sample()
# TODO：keep the same dimension with real covariance
if uncertainty_driven_exploration:
unc_a = np.zeros((act_dim, act_dim))
unc_rnd_a = np.zeros((act_dim, act_dim))
else:
unc_a = 0
unc_rnd_a = 0
q1_pred, q2_pred = 0, 0
q1_post = np.zeros((n_post_action, ))
q2_post = np.zeros((n_post_action, ))
unc_q1, unc_q2, unc_rnd_q = 0, 0, 0
unc_based_reward = 0
rnd_t_act, rnd_p_act, rnd_e_act, rnd_t_cri, rnd_p_cri, rnd_e_cri = 0, 0, 0, 0, 0, 0
# Sample an observation set to track their uncertainty trajectories
if t > start_steps:
if pi_unc_module.obs_set_is_empty:
pi_unc_module.sample_obs_set_from_replay_buffer(replay_buffer)
if t % pi_unc_module.track_obs_set_unc_frequency == 0:
pi_unc_module.calculate_obs_set_uncertainty(
sess, t // steps_per_epoch, t)
# TODO: try more frequent update to avoid bad dropout masks (perhaps not necessary because we can get
# larger sample size now.)
# Update uncertainty policy to current policy
if t % uncertainty_policy_delay == 0:
# pi_unc_module.uncertainty_policy_update(sess)
pi_unc_module.update_weights_of_main_unc(sess)
# Step the env
if render_env:
env.render()
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
ep_q1_var += np.var(q1_post)
ep_q2_var += np.var(q2_post)
# TODO: we cannot use this as uncertainty, because if the policy learns some dimension is correlated then the
# corresponding element will be 1 in covariance matrix.
ep_unc_a += np.sum(unc_a)
ep_unc_rnd_a += np.sum(unc_rnd_a)
ep_unc_q1 += unc_q1
ep_unc_q2 += unc_q2
ep_unc_rnd_q += unc_rnd_q
ep_rnd_e_act += rnd_e_act
ep_rnd_e_cri += rnd_e_cri
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o,
a,
reward_scale * r,
o2,
d,
t,
steps_per_epoch,
start_time,
unc_a=unc_a,
unc_rnd_a=unc_rnd_a,
unc_q1=unc_q1,
unc_q2=unc_q2,
unc_rnd_q=unc_rnd_q,
q1_pred=q1_pred,
q2_pred=q2_pred,
q1_post=q1_post,
q2_post=q2_post,
rnd_e_act=rnd_e_act,
rnd_e_cri=rnd_e_cri)
# replay_buffer.store(o, a, r + unc_based_reward, o2, d, uncertainty, t, steps_per_epoch, start_time)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if without_delay_train:
batch = replay_buffer.sample_batch(batch_size)
feed_dict_train[x_ph] = batch['obs1']
feed_dict_train[x2_ph] = batch['obs2']
feed_dict_train[a_ph] = batch['acts']
feed_dict_train[r_ph] = batch['rews']
feed_dict_train[d_ph] = batch['done']
# Train Random Net Distillation
rnd_step_ops_act = [
rnd_loss_act, rnd_targ_act, rnd_pred_act, train_rnd_op_act
]
rnd_outs_act = sess.run(rnd_step_ops_act, feed_dict_train)
logger.store(LossRnd=rnd_outs_act[0])
# Train q
q_step_ops = [q_loss, q1, q2, train_q_op]
outs = sess.run(q_step_ops, feed_dict_train)
logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2])
# Delayed policy update
outs = sess.run([pi_loss, train_pi_op, target_update],
feed_dict_train)
logger.store(LossPi=outs[0])
if d or (ep_len == max_ep_len):
"""
Perform all TD3 updates at the end of the trajectory
(in accordance with source code of TD3 published by
original authors).
"""
if not without_delay_train:
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict_train[x_ph] = batch['obs1']
feed_dict_train[x2_ph] = batch['obs2']
feed_dict_train[a_ph] = batch['acts']
feed_dict_train[r_ph] = batch['rews']
feed_dict_train[d_ph] = batch['done']
# Train Random Net Distillation
# change dropout masks every training step
mask_phs = [
rnd_pred_act_dropout_mask_phs,
rnd_pred_cri_dropout_mask_phs
]
mask_generators = [
rnd_pred_act_dropout_mask_generator,
rnd_pred_cri_dropout_mask_generator
]
feed_dict_train = set_dropout_mask_randomly_and_post_nuber_to_one(
feed_dict_train, mask_phs, mask_generators)
rnd_step_ops_act = [
rnd_loss_act, rnd_targ_act, rnd_pred_act,
train_rnd_op_act
]
rnd_outs_act = sess.run(rnd_step_ops_act, feed_dict_train)
logger.store(LossRndAct=rnd_outs_act[0])
rnd_step_ops_cri = [
rnd_loss_cri, rnd_targ_cri, rnd_pred_cri,
train_rnd_op_cri
]
rnd_outs_cri = sess.run(rnd_step_ops_cri, feed_dict_train)
logger.store(LossRndCri=rnd_outs_cri[0])
# Train Q-value function
# feed_dict_train[min_q_targ] = post_sample_q1_and_q2(feed_dict_train, batch_size)
q_step_ops = [q_loss, q1, q2, train_q_op]
outs = sess.run(q_step_ops, feed_dict_train)
logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2])
if j % policy_delay == 0:
# Delayed policy update
outs = sess.run([pi_loss, train_pi_op, target_update],
feed_dict_train)
logger.store(LossPi=outs[0])
# No weight update delay
pi_unc_module.update_weights_of_rnd_unc(sess)
logger.store(EpRet=ep_ret,
EpLen=ep_len,
EpQ1Var=ep_q1_var,
EpQ2Var=ep_q2_var,
EpUncAct=ep_unc_a,
EpUncRndAct=ep_unc_rnd_a,
EpUncQ1=ep_unc_q1,
EpUncQ2=ep_unc_q2,
EpUncRndQ=ep_unc_rnd_q,
EpRndErrorAct=ep_rnd_e_act,
EpRndErrorCri=ep_rnd_e_cri)
o, r, d, ep_ret, ep_len, ep_q1_var, ep_q2_var,\
ep_unc_a, ep_unc_q1, ep_unc_q2,\
ep_unc_rnd_a, ep_unc_rnd_q,\
ep_rnd_e_act, ep_rnd_e_cri = env.reset(), 0, False, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('LossRndAct', average_only=True)
logger.log_tabular('LossRndCri', average_only=True)
logger.log_tabular('EpQ1Var', with_min_and_max=True)
logger.log_tabular('EpQ2Var', with_min_and_max=True)
logger.log_tabular('EpUncAct', with_min_and_max=True)
logger.log_tabular('EpUncRndAct', with_min_and_max=True)
logger.log_tabular('EpUncQ1', with_min_and_max=True)
logger.log_tabular('EpUncQ2', with_min_and_max=True)
logger.log_tabular('EpUncRndQ', with_min_and_max=True)
logger.log_tabular('EpRndErrorAct', with_min_and_max=True)
logger.log_tabular('EpRndErrorCri', with_min_and_max=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def oac(env_fn, logger_kwargs=dict(), network_params=dict(), rl_params=dict()):
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# env params
thresh = rl_params['thresh']
# control params
seed = rl_params['seed']
epochs = rl_params['epochs']
steps_per_epoch = rl_params['steps_per_epoch']
replay_size = rl_params['replay_size']
batch_size = rl_params['batch_size']
start_steps = rl_params['start_steps']
max_ep_len = rl_params['max_ep_len']
max_noop = rl_params['max_noop']
save_freq = rl_params['save_freq']
render = rl_params['render']
# rl params
gamma = rl_params['gamma']
polyak = rl_params['polyak']
lr = rl_params['lr']
grad_clip_val = rl_params['grad_clip_val']
# entropy params
alpha = rl_params['alpha']
target_entropy_start = rl_params['target_entropy_start']
target_entropy_stop = rl_params['target_entropy_stop']
target_entropy_steps = rl_params['target_entropy_steps']
# optimistic exploration params
use_opt = rl_params['use_opt']
beta_UB = rl_params['beta_UB']
beta_LB = rl_params['beta_LB']
delta = rl_params['delta']
opt_lr = rl_params['opt_lr']
max_opt_steps = rl_params['max_opt_steps']
train_env, test_env = env_fn(), env_fn()
obs_space = env.observation_space
act_space = env.action_space
# get the size after resize
obs_dim = network_params['input_dims']
act_dim = act_space.n
# set the seed
tf.set_random_seed(seed)
np.random.seed(seed)
train_env.seed(seed)
train_env.action_space.np_random.seed(seed)
test_env.seed(seed)
test_env.action_space.np_random.seed(seed)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# init a state buffer for storing last m states
train_state_buffer = StateBuffer(m=obs_dim[2])
test_state_buffer = StateBuffer(m=obs_dim[2])
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = placeholders(obs_dim, act_dim, obs_dim,
None, None)
# alpha and entropy setup
max_target_entropy = tf.log(tf.cast(act_dim, tf.float32))
target_entropy_prop_ph = tf.placeholder(dtype=tf.float32, shape=())
target_entropy = max_target_entropy * target_entropy_prop_ph
log_alpha = tf.get_variable('log_alpha', dtype=tf.float32, initializer=0.0)
if alpha == 'auto': # auto tune alpha
alpha = tf.exp(log_alpha)
else: # fixed alpha
alpha = tf.get_variable('alpha', dtype=tf.float32, initializer=alpha)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, action_probs, log_action_probs, action_logits, q1_logits, q2_logits = build_models(
x_ph, a_ph, act_dim, network_params)
with tf.variable_scope('main', reuse=True):
_, _, action_probs_next, log_action_probs_next, _, _, _ = build_models(
x2_ph, a_ph, act_dim, network_params)
# Target value network
with tf.variable_scope('target'):
_, _, _, _, _, q1_logits_targ, q2_logits_targ = build_models(
x2_ph, a_ph, act_dim, network_params)
# Count variables
var_counts = tuple(
count_vars(scope)
for scope in ['log_alpha', 'main/pi', 'main/q1', 'main/q2', 'main'])
print("""\nNumber of parameters:
alpha: %d,
pi: %d,
q1: %d,
q2: %d,
total: %d\n""" % var_counts)
if use_opt:
# Optimistic Exploration
mu_Q = (q1_logits + q2_logits) / 2.0
sigma_Q = tf.math.abs(q1_logits - q2_logits) / 2.0
Q_UB = mu_Q + beta_UB * sigma_Q
Q_LB = mu_Q + beta_LB * sigma_Q
Q_UB_sm = tf.nn.softmax(
Q_UB, axis=-1
) # needed to make EV and penalty proportional for optimisation
R = tf.get_variable('R',
dtype=tf.float32,
shape=[1, act_dim],
initializer=tf.random_normal_initializer(
mean=0.0, stddev=0.01))
assign_R = R.assign(
action_logits
) # initialises P as the same "pessimistic" action distribution
P = tf.nn.softmax(R, axis=-1)
expected_value = tf.reduce_sum(tf.multiply(P, Q_UB_sm))
KL_P_PT = tf.reduce_sum(
tf.multiply(P, tf.log(tf.divide(P, action_probs))))
penalty = KL_P_PT - delta
relu_penalty = tf.nn.relu(penalty)
penalised_opt_function = -expected_value + relu_penalty
optpi_optimizer = tf.train.AdamOptimizer(learning_rate=opt_lr)
train_optpi_op = optpi_optimizer.minimize(penalised_opt_function,
var_list=get_vars('R'))
optimistic_policy_dist = tf.distributions.Categorical(probs=P)
optimistic_pi = optimistic_policy_dist.sample()
else:
optimistic_pi = pi # use standard SAC policy
Q_LB = tf.minimum(q1_logits, q2_logits)
# Min Double-Q:
min_q_logits_targ = tf.minimum(q1_logits_targ, q2_logits_targ)
# Targets for Q regression
q_backup = r_ph + gamma * (1 - d_ph) * tf.stop_gradient(
tf.reduce_sum(action_probs_next *
(min_q_logits_targ - alpha * log_action_probs_next),
axis=-1))
# critic losses
q1_a = tf.reduce_sum(tf.multiply(q1_logits, a_ph), axis=1)
q2_a = tf.reduce_sum(tf.multiply(q2_logits, a_ph), axis=1)
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1_a)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2_a)**2)
value_loss = q1_loss + q2_loss
# policy loss
pi_backup = tf.reduce_sum(action_probs * (alpha * log_action_probs - Q_LB),
axis=-1)
pi_loss = tf.reduce_mean(pi_backup)
# alpha loss for temperature parameter
pi_entropy = -tf.reduce_sum(action_probs * log_action_probs, axis=-1)
alpha_backup = tf.stop_gradient(target_entropy - pi_entropy)
alpha_loss = -tf.reduce_mean(log_alpha * alpha_backup)
# Policy train op
# (has to be separate from value train op, because q1_logits appears in pi_loss)
pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
if grad_clip_val is not None:
gvs = pi_optimizer.compute_gradients(pi_loss,
var_list=get_vars('main/pi'))
capped_gvs = [(ClipIfNotNone(grad, grad_clip_val), var)
for grad, var in gvs]
train_pi_op = pi_optimizer.apply_gradients(capped_gvs)
else:
train_pi_op = pi_optimizer.minimize(pi_loss,
var_list=get_vars('main/pi'))
# Value train op
# (control dep of train_pi_op because sess.run otherwise evaluates in nondeterministic order)
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
with tf.control_dependencies([train_pi_op]):
if grad_clip_val is not None:
gvs = value_optimizer.compute_gradients(
value_loss, var_list=get_vars('main/q'))
capped_gvs = [(ClipIfNotNone(grad, grad_clip_val), var)
for grad, var in gvs]
train_value_op = value_optimizer.apply_gradients(capped_gvs)
else:
train_value_op = value_optimizer.minimize(
value_loss, var_list=get_vars('main/q'))
# Alpha train op
alpha_optimizer = tf.train.AdamOptimizer(learning_rate=lr, epsilon=1e-04)
with tf.control_dependencies([train_value_op]):
train_alpha_op = alpha_optimizer.minimize(
alpha_loss, var_list=get_vars('log_alpha'))
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# All ops to call during one training step
step_ops = [
pi_loss, q1_loss, q2_loss, q1_a, q2_a, pi_entropy, target_entropy,
alpha_loss, alpha, train_pi_op, train_value_op, train_alpha_op,
target_update
]
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess = tf.Session(config=tf_config)
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph
},
outputs={
'mu': mu,
'pi': pi,
'q1_a': q1_a,
'q2_a': q2_a
})
def get_action(state, deterministic=False):
state = state.astype('float32') / 255.
# # record data for printing
# _ = sess.run(assign_R, feed_dict={x_ph: [state]})
# ins = sess.run([action_probs, Q_UB, P, KL_P_PT], feed_dict={x_ph: [state]})
if deterministic:
act_op = mu
else:
if use_opt:
# run a few optimisation steps to set optimistic policy
_ = sess.run(assign_R, feed_dict={x_ph: [state]})
for i in range(max_opt_steps):
_ = sess.run([train_optpi_op], feed_dict={x_ph: [state]})
act_op = optimistic_pi
# # print difference between pessimistic and optimistic policy probabilities
# outs = sess.run([P, KL_P_PT], feed_dict={x_ph: [state]})
# print('ap: ', ins[0])
# print('Q: ', ins[1])
# print('P_in: ', ins[2])
# print('P_out: ', outs[0])
# print('KL_in: ', ins[3])
# print('KL_out: ', outs[1])
# print('')
return sess.run(act_op, feed_dict={x_ph: [state]})[0]
def reset(env, state_buffer):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# fire to start game and perform no-op for some frames to randomise start
o, _, _, _ = env.step(1) # Fire action to start game
for _ in range(np.random.randint(1, max_noop)):
o, _, _, _ = env.step(0) # Action 'NOOP'
o = process_image_observation(o, obs_dim, thresh)
r = process_reward(r)
old_lives = env.ale.lives()
state = state_buffer.init_state(init_obs=o)
return o, r, d, ep_ret, ep_len, old_lives, state
def test_agent(n=10, render=True):
global sess, mu, pi, q1, q2
for j in range(n):
o, r, d, ep_ret, ep_len, test_old_lives, test_state = reset(
test_env, test_state_buffer)
terminal_life_lost_test = False
if render: test_env.render()
while not (d or (ep_len == max_ep_len)):
# start by firing
if terminal_life_lost_test:
a = 1
else:
# Take lower variance actions at test(noise_scale=0.05)
a = get_action(test_state, True)
# Take deterministic actions at test time
o, r, d, _ = test_env.step(a)
o = process_image_observation(o, obs_dim, thresh)
r = process_reward(r)
test_state = test_state_buffer.append_state(o)
ep_ret += r
ep_len += 1
if test_env.ale.lives() < test_old_lives:
test_old_lives = test_env.ale.lives()
terminal_life_lost_test = True
else:
terminal_life_lost_test = False
if render: test_env.render()
if render: test_env.close()
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
# ================== Main training Loop ==================
start_time = time.time()
o, r, d, ep_ret, ep_len, old_lives, state = reset(train_env,
train_state_buffer)
total_steps = steps_per_epoch * epochs
target_entropy_prop = linear_anneal(current_step=0,
start=target_entropy_start,
stop=target_entropy_stop,
steps=target_entropy_steps)
save_iter = 0
terminal_life_lost = False
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
# press fire to start
if terminal_life_lost:
a = 1
else:
if t > start_steps:
a = get_action(state)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
o2 = process_image_observation(o2, obs_dim, thresh)
r = process_reward(r)
one_hot_a = process_action(a, act_dim)
next_state = train_state_buffer.append_state(o2)
ep_ret += r
ep_len += 1
if train_env.ale.lives() < old_lives:
old_lives = train_env.ale.lives()
terminal_life_lost = True
else:
terminal_life_lost = False
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(state, one_hot_a, r, next_state,
terminal_life_lost)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
state = next_state
if d or (ep_len == max_ep_len):
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
target_entropy_prop_ph: target_entropy_prop
}
outs = sess.run(step_ops, feed_dict)
logger.store(LossPi=outs[0],
LossQ1=outs[1],
LossQ2=outs[2],
Q1Vals=outs[3],
Q2Vals=outs[4],
PiEntropy=outs[5],
TargEntropy=outs[6],
LossAlpha=outs[7],
Alpha=outs[8])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len, old_lives, state = reset(
train_env, train_state_buffer)
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# update target entropy every epoch
target_entropy_prop = linear_anneal(current_step=t,
start=target_entropy_start,
stop=target_entropy_stop,
steps=target_entropy_steps)
# Save model
if save_freq is not None:
if (epoch % save_freq == 0) or (epoch == epochs - 1):
print('Saving...')
logger.save_state({'env': env}, itr=save_iter)
save_iter += 1
# Test the performance of the deterministic version of the agent.
test_agent(n=10, render=render)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('PiEntropy', average_only=True)
logger.log_tabular('TargEntropy', average_only=True)
logger.log_tabular('Alpha', average_only=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossAlpha', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
plot_progress(os.path.join(logger_kwargs['output_dir'], 'progress.txt'),
show_plot=True)
def simple_dqn(env_fn = lambda : gym.make('CartPole-v1')
, actor_critic=None
, ac_kwargs=dict()
, seed=0
, episodes_per_epoch=1000
, epochs=1000
, gamma=0.99
, logger_kwargs=dict()
, save_freq=1000
, hidden_dim=32
, n_layers=2
, lr=1e-4
, batch_size=32
, target_update_freq=2500
, final_epsilon=0.05
, finish_decay=50000
, replay_buffer_size=25000
, steps_before_training=5000
, n_test_eps = 10
):
max_steps_per_epoch = 5000
# Global variables
num_of_train_epochs = epochs
# `number_of_layers` hidden layers with `hidden_dim` units each
number_of_layers = n_layers
learning_rate = lr
discount_factor = gamma
# init log
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
#make gym enviornment
env = env_fn()
obs_dim = env.observation_space.shape[0]
number_of_actions = env.action_space.n
#define evaluation network
with tf.variable_scope('evaluation_network'):
#input layer
obs_ph = tf.placeholder(dtype=tf.float32, shape=(None,obs_dim), name='obs_ph')
#mlp - #mlp (Multi Layer Perceptron) - hidden layers
hidden_sizes = [hidden_dim] * number_of_layers
x = obs_ph
for h in hidden_sizes:
x = tf.layers.dense(x, units=h, activation=tf.tanh)
#output layer
eval_net = tf.layers.dense(x,units=number_of_actions,activation=None)
#define taget network
with tf.variable_scope('target_network'):
#input layer
obs_target_ph = tf.placeholder(dtype=tf.float32, shape=(None,obs_dim), name='obs_target_ph')
#mlp - #mlp (Multi Layer Perceptron) - hidden layers
hidden_sizes = [hidden_dim] * number_of_layers
x = obs_target_ph
for h in hidden_sizes:
x = tf.layers.dense(x, units=h, activation=tf.tanh)
#output layer
target_net = tf.layers.dense(x,units=number_of_actions,activation=None)
#define loss function
selected_action_ph = tf.placeholder(shape=(None,), dtype=tf.int32, name='selected_action_ph')
reward_ph = tf.placeholder(shape=(None,), dtype=tf.float32, name='reward_ph')
done_ph = tf.placeholder(shape=(None,), dtype=tf.float32, name='done_ph')
actions_one_hot = tf.one_hot(selected_action_ph, number_of_actions)
q_a = tf.reduce_sum(actions_one_hot * eval_net,axis=1)
#use target network to approximate TD
target = reward_ph + discount_factor * (1-done_ph) * tf.stop_gradient(tf.reduce_max(target_net, axis=1))
loss = tf.reduce_mean((q_a - target)**2)
#init replay buffer
replay_current_obs = np.zeros([replay_buffer_size, obs_dim], dtype=np.int32)
replay_next_obs = np.zeros([replay_buffer_size, obs_dim], dtype=np.int32)
replay_selected_action = np.zeros(replay_buffer_size, dtype=np.int32)
replay_reward =np.zeros(replay_buffer_size, dtype=np.float32)
replay_done = np.zeros(replay_buffer_size, dtype=np.float32)
# update op for target network
main_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='evaluation_network')
target_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_network')
assign_ops = [tf.assign(target_var, main_var) for target_var, main_var in zip(target_vars, main_vars)]
target_update_op = tf.group(*assign_ops)
# define train optimizer_operation
optimizer_operation = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
# init session
session = tf.InteractiveSession()
session.run(tf.global_variables_initializer())
logger.setup_tf_saver(session, inputs={'x': obs_ph}, outputs={'q': eval_net})
current_index = replay_buffer_size - 1
#reset train data
epoch, step, training_finished, epsilon = 0, 0, False, 1
#reset epoch data
epoch_rews, epoch_lens, epoch_losses, epoch_qs = [], [], [], []
#reset episodic data
obs, reward, done, ep_rews, ep_len, episode_num, end_of_epoch = env.reset(), 0, False, 0, 0, 0, False
last_number_steps = 0
while not training_finished:
step += 1
#get action -
# epsilon greedy
selected_action = 0
if np.random.rand() < epsilon :
#exploration
selected_action = np.random.randint(number_of_actions)
else:
#exploitation
estimated_q = session.run(eval_net, feed_dict={obs_ph: obs.reshape(1,-1)})
selected_action = np.argmax(estimated_q)
# preform one step in gym enviornment
# receive observation reward and whether the episode has ended
obs, reward, done, _ = env.step(selected_action)
#store information in replay buffer
#TODO deal with first and done
replay_next_obs[current_index] = obs
current_index = step % replay_buffer_size
replay_current_obs[current_index] = obs
replay_selected_action[current_index] = selected_action
replay_reward[current_index] = reward
replay_done[current_index] = done
ep_rews += reward
ep_len += 1
if done:
episode_num += 1
#save episodic data
epoch_rews.append(ep_rews)
epoch_lens.append(ep_len)
#reset episodic data
obs, reward, done, ep_rews, ep_len, end_of_epoch = env.reset(), 0, False, 0, 0, episode_num % episodes_per_epoch == 0
#first `steps_before_training` do no train - replay buffer is too small
if step > steps_before_training:
#single train iteration
#get data from replay
trained_indices = np.random.randint(min(replay_buffer_size, step), size = batch_size)
trained_observation = replay_current_obs[trained_indices]
trained_next_observation = replay_next_obs[trained_indices]
trained_selected_action = replay_selected_action[trained_indices]
trained_reward = replay_reward[trained_indices]
trained_done = replay_done[trained_indices]
#if (step % save_freq == 0) or (step >= total_number_of_steps - 1):
# logger.save_state({'env': env}, None)
# train eval network
step_loss, curr_q, _ = session.run([loss, q_a, optimizer_operation], feed_dict={obs_ph: trained_observation,
obs_target_ph: trained_next_observation,
selected_action_ph: trained_selected_action,
reward_ph: trained_reward,
done_ph: trained_done})
#just for logging
epoch_losses.append(step_loss)
epoch_qs.append(curr_q)
if end_of_epoch:
logger.save_state({'env': env}, None)
# update target network
session.run(target_update_op)
epoch += 1
training_finished = epoch >= num_of_train_epochs
#test epoch
ep_rets, ep_lens = [], []
for _ in range(n_test_eps):
obs, rew, done, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not(done):
#env.render()
estimated_q = session.run(eval_net, feed_dict={obs_ph: obs.reshape(1,-1)})
selected_action = np.argmax(estimated_q)
obs, rew, done, _ = env.step(selected_action)
ep_ret += rew
ep_len += 1
ep_rets.append(ep_ret)
ep_lens.append(ep_len)
test_ep_ret = np.mean(ep_rets)
test_ep_len = np.mean(ep_lens)
obs, rew, done, ep_ret, ep_len, end_of_epoch = env.reset(), 0, False, 0, 0, False
# log epoch results
logger.log_tabular('Epoch', epoch)
logger.log_tabular('TotalEnvInteracts', step - last_number_steps)
logger.log_tabular('loss', np.mean(epoch_losses))
logger.log_tabular('AverageEpRet', np.mean(test_ep_ret))
logger.log_tabular('epispode mean length', np.mean(test_ep_len))
logger.dump_tabular()
epoch_rews, epoch_lens, epoch_losses, epoch_qs, last_number_steps= [], [], [], [], step
#adapt epsilon
epsilon = 1 + (final_epsilon - 1)*min(1, step/finish_decay)
def my_ddpg(env_fn, seed=0, steps_per_epoch=4000, epochs=100, max_ep_len=1000,
hidden_sizes=[256,256],
logger_kwargs=dict(), save_freq=1,
batch_size=100, start_steps=10000,
update_after=1000, update_every=50, num_test_episodes=10,
gamma=0.99, polyak=0.995, act_noise=0.1,
pi_lr=1e-3, q_lr=1e-3, buffer_size=int(1e6)):
"""
My DDPG implementation
"""
# Set up logger and save configuration
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Random seed
torch.manual_seed(seed)
np.random.seed(seed)
# Instantiate environment
env = env_fn()
test_env = env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
print("env.observation_space", env.observation_space)
print("env.observation_space.shape", env.observation_space.shape)
print("env.action_space", env.action_space)
action_min = env.action_space.low[0]
action_max = env.action_space.high[0]
if isinstance(env.action_space, gym.spaces.Discrete):
print("Discrete action space not supported for my-ddpg!")
return
# Set up experience buffer
buf = ReplayBuffer(obs_dim, act_dim, buffer_size)
# Instantiate models
assert action_max == abs(action_min)
policy = DeterministicPolicyNet(obs_dim, act_dim, hidden_sizes, action_max)
policy_target = copy.deepcopy(policy)
policy_optimizer = torch.optim.Adam(policy.mu_net.parameters(), lr=pi_lr)
q_function = QNet(obs_dim, act_dim, hidden_sizes)
q_function_target = copy.deepcopy(q_function)
q_optimizer = torch.optim.Adam(q_function.q_net.parameters(), lr=q_lr)
# Set up model saving
logger.setup_pytorch_saver(policy)
# TODO: Save value network as well
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p_targ in policy_target.parameters():
p_targ.requires_grad = False
for q_targ in q_function_target.parameters():
q_targ.requires_grad = False
# Prepare for interaction with environment
num_steps = epochs * steps_per_epoch
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for step in range(num_steps): # TODO: Change to for loop over range(epochs) and range(steps_per_epoch)
with torch.no_grad():
if step < start_steps:
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy (with some noise, via act_noise).
a = env.action_space.sample()
else:
assert o.shape == (obs_dim,)
a = policy(torch.tensor(o, dtype=torch.float32).unsqueeze(0))
assert a.shape == (1, act_dim)
a = a[0] # Remove batch dimension
a = torch.clamp(a + act_noise * torch.randn(act_dim), action_min, action_max) # Add exploration noise
a = a.numpy() # Convert to numpy
next_o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len==max_ep_len else d
buf.store(o, a, r, next_o, d)
# Update obs (critical!)
o = next_o
# Trajectory finished
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
if step >= update_after and step % update_every == 0:
for _ in range(update_every):
def update():
o, a, r, next_o, d = buf.sample_batch(batch_size)
# Compute targets
with torch.no_grad():
next_a_targ = policy_target(next_o)
next_q_targ = q_function_target(next_o, next_a_targ)
q_targ = r + gamma * (1 - d) * next_q_targ
# Update Q function
q_optimizer.zero_grad()
q_loss = ((q_function(o, a) - q_targ)**2).mean()
q_loss.backward()
q_optimizer.step()
# Freeze Q-network so you don't waste computational effort
# computing gradients for it during the policy learning step.
for p in q_function.parameters():
p.requires_grad = False
# Policy function update
policy_optimizer.zero_grad()
policy_loss = -(q_function(o, policy(o))).mean()
policy_loss.backward()
policy_optimizer.step()
# Unfreeze Q-network so you can optimize it at next DDPG step.
for p in q_function.parameters():
p.requires_grad = True
# Update target networks with polyak
with torch.no_grad():
for p, p_targ in zip(policy.parameters(), policy_target.parameters()):
p_targ.data.mul_(polyak)
p_targ.data.add_((1 - polyak) * p.data)
for q, q_targ in zip(q_function.parameters(), q_function_target.parameters()):
q_targ.data.mul_(polyak)
q_targ.data.add_((1 - polyak) * q.data)
update()
if (step + 1) % steps_per_epoch == 0:
epoch = (step + 1) // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
def test_agent():
with torch.no_grad():
for j in range(num_test_episodes):
o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0
while not(d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
a = policy(torch.tensor(o, dtype=torch.float32).unsqueeze(0))
assert a.shape == (1, act_dim)
a = a[0] # Remove batch dimension
a = a.numpy() # Convert to numpy
o, r, d, _ = test_env.step(a)
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', step)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
def ppo(env_fn,
expert=None,
policy_path=None,
actor_critic=core.mlp_actor_critic_m,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=5000,
epochs=10000,
dagger_epochs=500,
pretrain_epochs=50,
gamma=0.99,
clip_ratio=0.2,
pi_lr=1e-4,
dagger_noise=0.01,
batch_size=64,
replay_size=int(5e3),
vf_lr=1e-4,
train_pi_iters=80,
train_v_iters=80,
lam=0.999,
max_ep_len=500,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10,
test_freq=10):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp`` (batch,) | Gives log probability, according to
| the policy, of taking actions ``a_ph``
| in states ``x_ph``.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``.
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``. (Critical: make sure
| to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to PPO.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
clip_ratio (float): Hyperparameter for clipping in the policy objective.
Roughly: how far can the new policy go from the old policy while
still profiting (improving the objective function)? The new policy
can still go farther than the clip_ratio says, but it doesn't help
on the objective anymore. (Usually small, 0.1 to 0.3.)
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_pi_iters (int): Maximum number of gradient descent steps to take
on policy loss per epoch. (Early stopping may cause optimizer
to take fewer than this.)
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
target_kl (float): Roughly what KL divergence we think is appropriate
between new and old policies after an update. This will get used
for early stopping. (Usually small, 0.01 or 0.05.)
policy_path (str): path of pretrained policy model
train from scratch if None
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
test_logger_kwargs = dict()
test_logger_kwargs['output_dir'] = osp.join(logger_kwargs['output_dir'],
"test")
test_logger_kwargs['exp_name'] = logger_kwargs['exp_name']
test_logger = EpochLogger(**test_logger_kwargs)
test_logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
act_high_limit = env.action_space.high
act_low_limit = env.action_space.low
sess = tf.Session()
if policy_path is None:
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space,
env.action_space)
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
tfa_ph = core.placeholder(act_dim)
# Main outputs from computation graph
mu, pi, logp, logp_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
sess.run(tf.global_variables_initializer())
else:
# load pretrained model
# sess, x_ph, a_ph, mu, pi, logp, logp_pi, v = load_policy(policy_path, itr='last', deterministic=False, act_high=env.action_space.high)
# # get_action_2 = lambda x : sess.run(mu, feed_dict={x_ph: x[None,:]})[0]
# adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
model = restore_tf_graph(sess, osp.join(policy_path, 'simple_save'))
x_ph, a_ph, adv_ph, ret_ph, logp_old_ph = model['x_ph'], model[
'a_ph'], model['adv_ph'], model['ret_ph'], model['logp_old_ph']
mu, pi, logp, logp_pi, v = model['mu'], model['pi'], model[
'logp'], model['logp_pi'], model['v']
# tfa_ph = core.placeholder(act_dim)
tfa_ph = model['tfa_ph']
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi]
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
print("---------------", local_steps_per_epoch)
buf = PPOBuffer(obs_dim, act_dim, steps_per_epoch, gamma, lam)
# print(obs_dim)
# print(act_dim)
dagger_replay_buffer = DaggerReplayBuffer(obs_dim=obs_dim[0],
act_dim=act_dim[0],
size=replay_size)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# PPO objectives
if policy_path is None:
ratio = tf.exp(logp - logp_old_ph) # pi(a|s) / pi_old(a|s)
min_adv = tf.where(adv_ph > 0, (1 + clip_ratio) * adv_ph,
(1 - clip_ratio) * adv_ph)
pi_loss = -tf.reduce_mean(tf.minimum(ratio * adv_ph, min_adv))
v_loss = tf.reduce_mean((ret_ph - v)**2)
dagger_pi_loss = tf.reduce_mean(tf.square(mu - tfa_ph))
# Info (useful to watch during learning)
approx_kl = tf.reduce_mean(
logp_old_ph -
logp) # a sample estimate for KL-divergence, easy to compute
approx_ent = tf.reduce_mean(
-logp) # a sample estimate for entropy, also easy to compute
clipped = tf.logical_or(ratio > (1 + clip_ratio), ratio <
(1 - clip_ratio))
clipfrac = tf.reduce_mean(tf.cast(clipped, tf.float32))
# Optimizers
dagger_pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
optimizer_pi = tf.train.AdamOptimizer(learning_rate=pi_lr)
optimizer_v = tf.train.AdamOptimizer(learning_rate=vf_lr)
train_dagger_pi_op = dagger_pi_optimizer.minimize(
dagger_pi_loss, name='train_dagger_pi_op')
train_pi = optimizer_pi.minimize(pi_loss, name='train_pi_op')
train_v = optimizer_v.minimize(v_loss, name='train_v_op')
sess.run(tf.variables_initializer(optimizer_pi.variables()))
sess.run(tf.variables_initializer(optimizer_v.variables()))
sess.run(tf.variables_initializer(dagger_pi_optimizer.variables()))
else:
graph = tf.get_default_graph()
dagger_pi_loss = model['dagger_pi_loss']
pi_loss = model['pi_loss']
v_loss = model['v_loss']
approx_ent = model['approx_ent']
approx_kl = model['approx_kl']
clipfrac = model['clipfrac']
train_dagger_pi_op = graph.get_operation_by_name('train_dagger_pi_op')
train_pi = graph.get_operation_by_name('train_pi_op')
train_v = graph.get_operation_by_name('train_v_op')
# sess = tf.Session()
# sess.run(tf.global_variables_initializer())
# Sync params across processes
# sess.run(sync_all_params())
tf.summary.FileWriter("log/", sess.graph)
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x_ph': x_ph, 'a_ph': a_ph, 'tfa_ph': tfa_ph, 'adv_ph': adv_ph, 'ret_ph': ret_ph, 'logp_old_ph': logp_old_ph}, \
outputs={'mu': mu, 'pi': pi, 'v': v, 'logp': logp, 'logp_pi': logp_pi, 'clipfrac': clipfrac, 'approx_kl': approx_kl, \
'pi_loss': pi_loss, 'v_loss': v_loss, 'dagger_pi_loss': dagger_pi_loss, 'approx_ent': approx_ent})
def update():
inputs = {k: v for k, v in zip(all_phs, buf.get())}
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Training
for i in range(train_pi_iters):
_, kl = sess.run([train_pi, approx_kl], feed_dict=inputs)
kl = mpi_avg(kl)
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
logger.store(StopIter=i)
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
pi_l_new, v_l_new, kl, cf = sess.run(
[pi_loss, v_loss, approx_kl, clipfrac], feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
def choose_action(s, add_noise=False):
s = s[np.newaxis, :]
a = sess.run(mu, {x_ph: s})[0]
if add_noise:
noise = dagger_noise * act_high_limit * np.random.normal(
size=a.shape)
a = a + noise
return np.clip(a, act_low_limit, act_high_limit)
def test_agent(n=81, test_num=1):
n = env.unwrapped._set_test_mode(True)
con_flag = False
for j in range(n):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, info = env.step(choose_action(np.array(o), 0))
ep_ret += r
ep_len += 1
if d:
test_logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_logger.store(arrive_des=info['arrive_des'])
test_logger.store(
arrive_des_appro=info['arrive_des_appro'])
if not info['out_of_range']:
test_logger.store(converge_dis=info['converge_dis'])
con_flag = True
test_logger.store(out_of_range=info['out_of_range'])
# print(info)
# test_logger.dump_tabular()
# time.sleep(10)
if not con_flag:
test_logger.store(converge_dis=10000)
env.unwrapped._set_test_mode(False)
def ref_test_agent(n=81, test_num=1):
n = env.unwrapped._set_test_mode(True)
con_flag = False
for j in range(n):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
a = call_ref_controller(env, expert)
o, r, d, info = env.step(a)
ep_ret += r
ep_len += 1
if d:
test_logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_logger.store(arrive_des=info['arrive_des'])
test_logger.store(
arrive_des_appro=info['arrive_des_appro'])
if not info['out_of_range']:
test_logger.store(converge_dis=info['converge_dis'])
con_flag = True
test_logger.store(out_of_range=info['out_of_range'])
# print(info)
# test_logger.dump_tabular()
if not con_flag:
test_logger.store(converge_dis=10000)
env.unwrapped._set_test_mode(False)
ref_test_agent(test_num=-1)
test_logger.log_tabular('epoch', -1)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('arrive_des_appro', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
test_policy_epochs = 91
episode_steps = 500
total_env_t = 0
test_num = 0
print(colorize("begin dagger training", 'green', bold=True))
for epoch in range(1, dagger_epochs + 1, 1):
# test policy
if epoch > 0 and (epoch % save_freq == 0) or (epoch == epochs):
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('arrive_des_appro', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
# train policy
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
env.unwrapped._set_test_mode(False)
obs, acs, rewards = [], [], []
for t in range(local_steps_per_epoch):
a, v_t, logp_t = sess.run(
get_action_ops, feed_dict={x_ph: np.array(o).reshape(1, -1)})
# a = get_action_2(np.array(o))
# save and log
obs.append(o)
ref_action = call_ref_controller(env, expert)
if (epoch < pretrain_epochs):
action = ref_action
else:
action = choose_action(np.array(o), True)
buf.store(o, action, r, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(action)
acs.append(ref_action)
rewards.append(r)
ep_ret += r
ep_len += 1
total_env_t += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t == local_steps_per_epoch - 1):
if not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = r if d else sess.run(
v, feed_dict={x_ph: np.array(o).reshape(1, -1)})
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Perform dagger and partical PPO update!
inputs = {k: v for k, v in zip(all_phs, buf.get())}
# pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent], feed_dict=inputs)
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
max_step = len(np.array(rewards))
dagger_replay_buffer.stores(obs, acs, rewards)
for _ in range(int(local_steps_per_epoch / 10)):
batch = dagger_replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'], tfa_ph: batch['acts']}
q_step_ops = [dagger_pi_loss, train_dagger_pi_op]
for j in range(10):
outs = sess.run(q_step_ops, feed_dict)
logger.store(LossPi=outs[0])
c_v_loss = sess.run(v_loss, feed_dict=inputs)
logger.store(LossV=c_v_loss,
KL=0,
Entropy=0,
ClipFrac=0,
DeltaLossPi=0,
DeltaLossV=0,
StopIter=0)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
# Main loop: collect experience in env and update/log each epoch
print(colorize("begin ppo training", 'green', bold=True))
for epoch in range(1, epochs + 1, 1):
# test policy
if epoch > 0 and (epoch % save_freq == 0) or (epoch
== epochs) or epoch == 1:
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('arrive_des_appro', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
# train policy
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
env.unwrapped._set_test_mode(False)
for t in range(local_steps_per_epoch):
a, v_t, logp_t = sess.run(
get_action_ops, feed_dict={x_ph: np.array(o).reshape(1, -1)})
# a = a[0]
# a = get_action_2(np.array(o))
# a = np.clip(a, act_low_limit, act_high_limit)
# if epoch < pretrain_epochs:
# a = env.action_space.sample()
# a = np.clip(a, act_low_limit, act_high_limit)
# save and log
buf.store(o, a, r, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(a[0])
ep_ret += r
ep_len += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t == local_steps_per_epoch - 1):
if not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = r if d else sess.run(
v, feed_dict={x_ph: np.array(o).reshape(1, -1)})
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Perform PPO update!
update()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sac_pytorch(
env_fn,
hidden_sizes=[256, 256],
seed=0,
steps_per_epoch=5000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=3e-4,
alpha=0.2,
batch_size=256,
start_steps=10000,
max_ep_len=1000,
save_freq=1,
dont_save=True,
regularization_weight=0,
grad_clip=-1,
logger_kwargs=dict(),
):
"""
Largely following OpenAI documentation
But slightly different from tensorflow implementation
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
hidden_sizes: number of entries is number of hidden layers
each entry in this list indicate the size of that hidden layer.
applies to all networks
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch. Note the epoch here is just logging epoch
so every this many steps a logging to stdouot and also output file will happen
note: not to be confused with training epoch which is a term used often in literature for all kinds of
different things
epochs (int): Number of epochs to run and train agent. Usage of this term can be different in different
algorithms, use caution. Here every epoch you get new logs
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration. However during testing the action always come from policy
max_ep_len (int): Maximum length of trajectory / episode / rollout. Environment will get reseted if
timestep in an episode excedding this number
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
logger_kwargs (dict): Keyword args for EpochLogger.
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("running on device:", device)
"""set up logger"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
env, test_env = env_fn(), env_fn()
## seed torch and numpy
torch.manual_seed(seed)
np.random.seed(seed)
## seed environment along with env action space so that everything about env is seeded
env.seed(seed)
env.action_space.np_random.seed(seed)
test_env.seed(seed)
test_env.action_space.np_random.seed(seed)
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# if environment has a smaller max episode length, then use the environment's max episode length
max_ep_len = env._max_episode_steps if max_ep_len > env._max_episode_steps else max_ep_len
# Action limit for clamping: critically, assumes all dimensions share the same bound!
# we need .item() to convert it from numpy float to python float
act_limit = env.action_space.high[0].item()
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
def test_agent(n=5):
"""
This will test the agent's performance by running n episodes
During the runs, the agent only take deterministic action, so the
actions are not drawn from a distribution, but just use the mean
:param n: number of episodes to run the agent
"""
ep_return_list = np.zeros(n)
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
a = policy_net.get_env_action(o, deterministic=True)
o, r, d, _ = test_env.step(a)
ep_ret += r
ep_len += 1
ep_return_list[j] = ep_ret
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
"""init all networks"""
# see line 1
policy_net = TanhGaussianPolicy(obs_dim,
act_dim,
hidden_sizes,
action_limit=act_limit).to(device)
value_net = Mlp(obs_dim, 1, hidden_sizes).to(device)
target_value_net = Mlp(obs_dim, 1, hidden_sizes).to(device)
q1_net = Mlp(obs_dim + act_dim, 1, hidden_sizes).to(device)
q2_net = Mlp(obs_dim + act_dim, 1, hidden_sizes).to(device)
# see line 2: copy parameters from value_net to target_value_net
target_value_net.load_state_dict(value_net.state_dict())
# set up optimizers
policy_optimizer = optim.Adam(policy_net.parameters(), lr=lr)
value_optimizer = optim.Adam(value_net.parameters(), lr=lr)
q1_optimizer = optim.Adam(q1_net.parameters(), lr=lr)
q2_optimizer = optim.Adam(q2_net.parameters(), lr=lr)
# mean squared error loss for v and q networks
mse_criterion = nn.MSELoss()
# Main loop: collect experience in env and update/log each epoch
# NOTE: t here is the current number of total timesteps used
# it is not the number of timesteps passed in the current episode
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > start_steps:
a = policy_net.get_env_action(o, deterministic=False)
else:
a = env.action_space.sample()
# Step the env, get next observation, reward and done signal
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience (observation, action, reward, next observation, done) to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
Quoted from the original SAC paper: 'In practice, we take a single environment step
followed by one or several gradient step' after a single environment step,
the number of gradient steps is 1 for SAC. (see paper for reference)
"""
for j in range(ep_len):
# get data from replay buffer
batch = replay_buffer.sample_batch(batch_size)
obs_tensor = Tensor(batch['obs1']).to(device)
obs_next_tensor = Tensor(batch['obs2']).to(device)
acts_tensor = Tensor(batch['acts']).to(device)
# unsqueeze is to make sure rewards and done tensors are of the shape nx1, instead of n
# to prevent problems later
rews_tensor = Tensor(batch['rews']).unsqueeze(1).to(device)
done_tensor = Tensor(batch['done']).unsqueeze(1).to(device)
"""
now we do a SAC update, following the OpenAI spinup doc
check the openai sac document psudocode part for reference
line nubmers indicate lines in psudocode part
we will first compute each of the losses
and then update all the networks in the end
"""
# see line 12: get a_tilda, which is newly sampled action (not action from replay buffer)
a_tilda, mean_a_tilda, log_std_a_tilda, log_prob_a_tilda, _, _ = policy_net.forward(
obs_tensor)
"""get q loss"""
# see line 12: first equation
v_from_target_v_net = target_value_net(obs_next_tensor)
y_q = rews_tensor + gamma * (1 -
done_tensor) * v_from_target_v_net
# see line 13: compute loss for the 2 q networks, note that we want to detach the y_q value
# since we only want to update q networks here, and don't want other gradients
q1_prediction = q1_net(torch.cat([obs_tensor, acts_tensor], 1))
q1_loss = mse_criterion(q1_prediction, y_q.detach())
q2_prediction = q2_net(torch.cat([obs_tensor, acts_tensor], 1))
q2_loss = mse_criterion(q2_prediction, y_q.detach())
"""get v loss"""
# see line 12: second equation
q1_a_tilda = q1_net(torch.cat([obs_tensor, a_tilda], 1))
q2_a_tilda = q2_net(torch.cat([obs_tensor, a_tilda], 1))
min_q1_q2_a_tilda = torch.min(
torch.cat([q1_a_tilda, q2_a_tilda], 1),
1)[0].reshape(-1, 1)
y_v = min_q1_q2_a_tilda - alpha * log_prob_a_tilda
# see line 14: compute loss for value network
v_prediction = value_net(obs_tensor)
v_loss = mse_criterion(v_prediction, y_v.detach())
"""policy loss"""
# line 15: note that here we are doing gradient ascent, so we add a minus sign in the front
policy_loss = -(q1_a_tilda - alpha * log_prob_a_tilda).mean()
"""
add policy regularization loss, this is not in openai's minimal version, but
they are in the original sac code, see https://github.com/vitchyr/rlkit for reference
this part is not necessary but might improve performance
"""
if regularization_weight > 0:
policy_mean_reg_weight = regularization_weight
policy_std_reg_weight = regularization_weight
mean_reg_loss = policy_mean_reg_weight * (mean_a_tilda**
2).mean()
std_reg_loss = policy_std_reg_weight * (log_std_a_tilda**
2).mean()
policy_loss = policy_loss + mean_reg_loss + std_reg_loss
"""update networks"""
q1_optimizer.zero_grad()
q1_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(q1_net.parameters(), grad_clip)
q1_optimizer.step()
q2_optimizer.zero_grad()
q2_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(q2_net.parameters(), grad_clip)
q2_optimizer.step()
value_optimizer.zero_grad()
v_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(value_net.parameters(), grad_clip)
value_optimizer.step()
policy_optimizer.zero_grad()
policy_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(policy_net.parameters(),
grad_clip)
policy_optimizer.step()
# see line 16: update target value network with value network
soft_update_model1_with_model2(target_value_net, value_net,
polyak)
# store diagnostic info to logger
logger.store(LossPi=policy_loss.cpu().item(),
LossQ1=q1_loss.cpu().item(),
LossQ2=q2_loss.cpu().item(),
LossV=v_loss.cpu().item(),
Q1Vals=q1_prediction.detach().cpu().numpy(),
Q2Vals=q2_prediction.detach().cpu().numpy(),
VVals=v_prediction.detach().cpu().numpy(),
LogPi=log_prob_a_tilda.detach().cpu().numpy())
## store episode return and length to logger
logger.store(EpRet=ep_ret, EpLen=ep_len)
## reset environment
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# End of epoch wrap-up
if (t + 1) % steps_per_epoch == 0:
epoch = t // steps_per_epoch
"""
Save pytorch model, very different from tensorflow version
We need to save the environment, the state_dict of each network
and also the state_dict of each optimizer
"""
if not dont_save:
sac_state_dict = {
'env': env,
'policy_net': policy_net.state_dict(),
'value_net': value_net.state_dict(),
'target_value_net': target_value_net.state_dict(),
'q1_net': q1_net.state_dict(),
'q2_net': q2_net.state_dict(),
'policy_opt': policy_optimizer,
'value_opt': value_optimizer,
'q1_opt': q1_optimizer,
'q2_opt': q2_optimizer
}
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state(sac_state_dict, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sac(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=1e-3,
alpha=0.2,
batch_size=100,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1):
"""
Soft Actor-Critic (SAC)
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: The constructor method for a PyTorch Module with an ``act``
method, a ``pi`` module, a ``q1`` module, and a ``q2`` module.
The ``act`` method and ``pi`` module should accept batches of
observations as inputs, and ``q1`` and ``q2`` should accept a batch
of observations and a batch of actions as inputs. When called,
``act``, ``q1``, and ``q2`` should return:
=========== ================ ======================================
Call Output Shape Description
=========== ================ ======================================
``act`` (batch, act_dim) | Numpy array of actions for each
| observation.
``q1`` (batch,) | Tensor containing one current estimate
| of Q* for the provided observations
| and actions. (Critical: make sure to
| flatten this!)
``q2`` (batch,) | Tensor containing the other current
| estimate of Q* for the provided observations
| and actions. (Critical: make sure to
| flatten this!)
=========== ================ ======================================
Calling ``pi`` should return:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``a`` (batch, act_dim) | Tensor containing actions from policy
| given observations.
``logp_pi`` (batch,) | Tensor containing log probabilities of
| actions in ``a``. Importantly: gradients
| should be able to flow back into ``a``.
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
you provided to SAC.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
update_after (int): Number of env interactions to collect before
starting to do gradient descent updates. Ensures replay buffer
is full enough for useful updates.
update_every (int): Number of env interactions that should elapse
between gradient descent updates. Note: Regardless of how long
you wait between updates, the ratio of env steps to gradient steps
is locked to 1.
num_test_episodes (int): Number of episodes to test the deterministic
policy at the end of each epoch.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Create actor-critic module and target networks
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
ac_targ = deepcopy(ac)
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in ac_targ.parameters():
p.requires_grad = False
# List of parameters for both Q-networks (save this for convenience)
q_params = itertools.chain(ac.q1.parameters(), ac.q2.parameters())
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables (protip: try to get a feel for how different size networks behave!)
var_counts = tuple(
core.count_vars(module) for module in [ac.pi, ac.q1, ac.q2])
logger.log('\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d\n' %
var_counts)
# Set up function for computing SAC Q-losses
def compute_loss_q(data):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
q1 = ac.q1(o, a)
q2 = ac.q2(o, a)
# Bellman backup for Q functions
with torch.no_grad():
# Target actions come from *current* policy
a2, logp_a2 = ac.pi(o2)
# Target Q-values
q1_pi_targ = ac_targ.q1(o2, a2)
q2_pi_targ = ac_targ.q2(o2, a2)
q_pi_targ = torch.min(q1_pi_targ, q2_pi_targ)
backup = r + gamma * (1 - d) * (q_pi_targ - alpha * logp_a2)
# MSE loss against Bellman backup
loss_q1 = ((q1 - backup)**2).mean()
loss_q2 = ((q2 - backup)**2).mean()
loss_q = loss_q1 + loss_q2
# Useful info for logging
q_info = dict(Q1Vals=q1.detach().numpy(), Q2Vals=q2.detach().numpy())
return loss_q, q_info
# Set up function for computing SAC pi loss
def compute_loss_pi(data):
o = data['obs']
pi, logp_pi = ac.pi(o)
q1_pi = ac.q1(o, pi)
q2_pi = ac.q2(o, pi)
q_pi = torch.min(q1_pi, q2_pi)
# Entropy-regularized policy loss
loss_pi = (alpha * logp_pi - q_pi).mean()
# Useful info for logging
pi_info = dict(LogPi=logp_pi.detach().numpy())
return loss_pi, pi_info
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=lr)
q_optimizer = Adam(q_params, lr=lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data):
# First run one gradient descent step for Q1 and Q2
q_optimizer.zero_grad()
loss_q, q_info = compute_loss_q(data)
loss_q.backward()
q_optimizer.step()
# Record things
logger.store(LossQ=loss_q.item(), **q_info)
# Freeze Q-networks so you don't waste computational effort
# computing gradients for them during the policy learning step.
for p in q_params:
p.requires_grad = False
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
loss_pi.backward()
pi_optimizer.step()
# Unfreeze Q-networks so you can optimize it at next DDPG step.
for p in q_params:
p.requires_grad = True
# Record things
logger.store(LossPi=loss_pi.item(), **pi_info)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(ac.parameters(), ac_targ.parameters()):
# NB: We use an in-place operations "mul_", "add_" to update target
# params, as opposed to "mul" and "add", which would make new tensors.
p_targ.data.mul_(polyak)
p_targ.data.add_((1 - polyak) * p.data)
def get_action(o, deterministic=False):
return ac.act(torch.as_tensor(o, dtype=torch.float32), deterministic)
def test_agent():
for j in range(num_test_episodes):
o, d, ep_ret, ep_len, success, goalDist, reachDist = test_env.reset(
), False, 0, 0, False, None, None
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
o, r, d, info = test_env.step(get_action(o, True))
ep_ret += r
ep_len += 1
if 'success' in info:
success = info['success'] or success
if 'goalDist' in info and info['goalDist'] is not None:
goalDist = info['goalDist']
if 'reachDist' in info and info['reachDist'] is not None:
reachDist = info['reachDist']
if goalDist != None:
logger.store(TestGoalDist=goalDist)
if reachDist != None:
logger.store(TestReachDist=reachDist)
logger.store(TestEpRet=ep_ret,
TestEpLen=ep_len,
TestSuccess=success)
# Prepare for interaction with environment
total_steps = steps_per_epoch * epochs
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
if t > start_steps:
a = get_action(o)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
# End of trajectory handling
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Update handling
if t >= update_after and t % update_every == 0:
for j in range(update_every):
batch = replay_buffer.sample_batch(batch_size)
update(data=batch)
# End of epoch handling
if (t + 1) % steps_per_epoch == 0:
epoch = (t + 1) // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
if 'TestGoalDist' in logger.epoch_dict:
logger.log_tabular('TestGoalDist', with_min_and_max=True)
if 'TestReachDist' in logger.epoch_dict:
logger.log_tabular('TestReachDist', with_min_and_max=True)
if 'TestSuccess' in logger.epoch_dict:
logger.log_tabular('TestSuccess', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def pretrain(env_fn, actor_critic=core.MLPActorCritic, ac_kwargs=dict(), seed=0,
steps_per_epoch=4000, epochs=50, pi_epochs=100, vf_epochs=50, gamma=0.99, clip_ratio=0.2, pi_lr=3e-4,
vf_lr=1e-3, train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000,
target_kl=0.01, logger_kwargs=dict(), save_freq=10, demo_file=""):
setup_pytorch_for_mpi()
logger = EpochLogger(**logger_kwargs)
# locals() return all local variable
logger.save_config(locals())
# Random seed
seed += 10000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# demo environment
demo_env = DemoGymEnv(demo_file=demo_file, seed=seed)
demo_env.check_env(env)
# Create actor-critic module
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs).to(device)
sync_params(ac)
# Set up experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = ACDFBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr)
vf_pi_optimizer = Adam(ac.v_pi.parameters(), lr=vf_lr)
logger.setup_pytorch_saver(ac)
def compute_loss_v(data):
obs, ret = Variable(data['obs']), Variable(data['ret'])
return ((ac.v(obs) - ret)**2).mean()
def compute_loss_v_pi(data):
obs, ret = Variable(data['obs']), Variable(data['ret'])
return ((ac.v_pi(obs) - ret)**2).mean()
def demo_update():
data = buf.get()
pi_l_old, pi_info_old = compute_loss_pi(data)
pi_l_old = pi_l_old.item()
v_l_old = compute_loss_v(data).item()
pi_info, loss_pi, loss_v = {}, 0, 0
for i in range(train_pi_iters):
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
kl = mpi_avg(pi_info['kl'])
if kl > 1.5 * target_kl:
# logger.log('Early stopping at step %d due to reaching max kl.' % i)
break
loss_pi.backward()
mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
logger.store(StopIter=i)
for i in range(train_v_iters):
vf_pi_optimizer.zero_grad()
loss_v = compute_loss_v_pi(data)
loss_v.backward()
mpi_avg_grads(ac.v_pi)
vf_pi_optimizer.step()
print("Pi loss: {}".format(pi_l_old))
kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf']
logger.store(LossPi=pi_l_old, LossV=v_l_old,
KL=kl, Entropy=ent, ClipFrac=cf,
DeltaLossPi=(loss_pi.item() - pi_l_old),
DeltaLossV=(loss_v.item() - v_l_old))
def compute_loss_pi(data):
obs, act, adv, logp_old = Variable(data['obs']), Variable(data['act']), Variable(data['adv']), Variable(data['logp'])
# Policy loss
pi, logp = ac.pi(obs, act)
ratio = torch.exp(logp - logp_old)
clip_adv = torch.clamp(ratio, 1-clip_ratio, 1+clip_ratio) * adv
loss_pi = -(torch.min(ratio * adv, clip_adv)).mean()
# Useful extra info
approx_kl = (logp_old - logp).mean().item()
ent = pi.entropy().mean().item()
clipped = ratio.gt(1+clip_ratio) | ratio.lt(1-clip_ratio)
clipfrac = torch.as_tensor(clipped, dtype=torch.float32, device=device).mean().item()
pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac)
return loss_pi, pi_info
buf = ACDFBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# pretraining epochs
# demonstration training: main loop, for policy network
o, ep_ret, ep_len = demo_env.reset(), 0, 0
start_time = time.time()
for epoch in range(pi_epochs):
pi_old_data = [deepcopy(p.data) for p in ac.pi.parameters()]
vf_old_data = [deepcopy(p.data) for p in ac.v.parameters()]
vf_pi_old_data = [deepcopy(p.data) for p in ac.v_pi.parameters()]
for t in range(local_steps_per_epoch):
a, v, logp_a, m, std = ac.pretrain_step(torch.as_tensor(o, dtype=torch.float32, device=device))
next_o, r, d, _ = demo_env.step(a, std)
ep_ret += r
ep_len += 1
buf.store(o, a, r, v, logp_a, std=std)
logger.store(VVals=v)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' % ep_len, flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
_, v, _, _, _ = ac.pretrain_step(torch.as_tensor(o, dtype=torch.float32, device=device))
else:
v = 0
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
buf.finish_path(v)
o, ep_ret, ep_len = demo_env.reset(), 0, 0
# Save model
# if (epoch % save_freq == 0) or (epoch == pi_epochs-1):
if (epoch in SAVE_FREQ) or (epoch == pi_epochs - 1):
save_pi(logger_kwargs.get('output_dir', "model"), itr=epoch, paramenters=ac.pi)
logger.save_state({'env': env}, None)
demo_update()
delta_v, delta_v_pi, delta_pi = 0, 0, 0
for i, param in enumerate(ac.v_pi.parameters()):
delta_v_pi += torch.norm(param.data - vf_pi_old_data[i])
for i, param in enumerate(ac.v.parameters()):
delta_v += torch.norm(param.data - vf_old_data[i])
for i, param in enumerate(ac.pi.parameters()):
delta_pi += torch.norm(param.data - pi_old_data[i])
print("delta v_pi: {}; delta vf: {}; delta pi: {}".format(delta_v_pi, delta_v, delta_pi))
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
logger.save_state({'env': env}, pi_epochs)
def update_vf():
data = buf.get()
v_l_old = compute_loss_v(data).item()
print("Loss for Value function: {}".format(v_l_old))
for i in range(train_v_iters):
vf_optimizer.zero_grad()
loss_v = compute_loss_v(data)
loss_v.backward()
mpi_avg_grads(ac.v)
vf_optimizer.step()
# for the value function pre-training
o, ep_ret, ep_len = demo_env.reset(), 0, 0
start_time = time.time()
for epoch in range(vf_epochs):
pi_old_data = [deepcopy(p.data) for p in ac.pi.parameters()]
vf_old_data = [deepcopy(p.data) for p in ac.v.parameters()]
vf_pi_old_data = [deepcopy(p.data) for p in ac.v_pi.parameters()]
for t in range(local_steps_per_epoch):
next_o, r, d, _, a = demo_env.free_step()
v = ac.v(torch.as_tensor(o, dtype=torch.float32, device=device)).cpu().detach().numpy()
ep_ret += r
ep_len += 1
buf.store(o, a, r, v, 1)
# logger.store(VVals=v)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' % ep_len, flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
v = ac.v(torch.as_tensor(o, dtype=torch.float32, device=device)).cpu().detach().numpy()
else:
v = 0
buf.finish_path(v)
o, ep_ret, ep_len = demo_env.reset(), 0, 0
print("Pretraining for value function at Epoch: {}".format(epoch))
update_vf()
delta_v, delta_v_pi, delta_pi = 0, 0, 0
for i, param in enumerate(ac.v_pi.parameters()):
delta_v_pi += torch.norm(param.data - vf_pi_old_data[i])
for i, param in enumerate(ac.v.parameters()):
delta_v += torch.norm(param.data - vf_old_data[i])
for i, param in enumerate(ac.pi.parameters()):
delta_pi += torch.norm(param.data - pi_old_data[i])
print("delta v_pi: {}; delta vf: {}; delta pi: {}".format(delta_v_pi, delta_v, delta_pi))
if (epoch in SAVE_FREQ) or (epoch == vf_epochs - 1):
save_vf(logger_kwargs.get('output_dir', "model"), itr=epoch, paramenters=ac.v)
logger.save_state({'env': env}, None)
def ddpg(env_fn,
actor_critic=core.ActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=5000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
pi_lr=1e-3,
q_lr=1e-3,
batch_size=100,
start_steps=10000,
act_noise=0.1,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A reference to ActorCritic class which after instantiation
takes state, ``x``, and action, ``a``, and returns:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Deterministically computes actions
| from policy given states.
``q`` (batch,) | Gives the current estimate of Q* for
| states in ``x`` and actions in
| ``a``.
``q_pi`` (batch,) | Gives the composition of ``q`` and
| ``pi`` for states in ``x``:
| q(x, pi(x)).
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to DDPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
pi_lr (float): Learning rate for policy.
q_lr (float): Learning rate for Q-networks.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
act_noise (float): Stddev for Gaussian exploration noise added to
policy at training time. (At test time, no noise is added.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
# https://pytorch.org/docs/master/notes/randomness.html#cudnn
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
actor_critic_main = actor_critic(obs_dim, **ac_kwargs).to(device)
# Note that the action placeholder going to targer actor_critic here
# is irrelevant, because we only need q_targ(s, pi_targ(s)).
actor_critic_target = actor_critic(obs_dim, **ac_kwargs).to(device)
# Count variables
var_counts = tuple(
core.count_vars(model) for model in
[actor_critic_main.policy, actor_critic_main.q, actor_critic_main])
logger.log('\nNumber of parameters: \t pi: %d, \t q: %d, \t total: %d\n' %
var_counts)
# Optimizers
pi_optimizer = optim.Adam(actor_critic_main.policy.parameters(), lr=pi_lr)
q_optimizer = optim.Adam(actor_critic_main.q.parameters(), lr=q_lr)
def get_action(o, noise_scale):
a = actor_critic_main(Tensor(o.reshape(1, -1)).to(device))
a = a.cpu().detach().numpy()
a += noise_scale * np.random.randn(act_dim)
return np.clip(a, -act_limit, act_limit)
def test_agent(n=10):
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, _ = test_env.step(get_action(o, 0))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy (with some noise, via act_noise).
"""
if t > start_steps:
a = get_action(o, act_noise)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
"""
Perform all DDPG updates at the end of the trajectory,
in accordance with tuning done by TD3 paper authors.
"""
for _ in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
x, x2, a, r, d = [
Tensor(batch[k]).to(device)
for k in ['obs1', 'obs2', 'acts', 'rews', 'done']
]
_, q, q_pi = actor_critic_main(x, a)
_, _, q_pi_targ = actor_critic_target(x2, a)
# Bellman backup for Q function
backup = (r + gamma * (1 - d) * q_pi_targ).detach()
# DDPG losses
pi_loss = -q_pi.mean()
q_loss = ((q - backup)**2).mean()
# Q-learning update
q_optimizer.zero_grad()
q_loss.backward()
q_optimizer.step()
logger.store(LossQ=q_loss, QVals=q.cpu().detach().numpy())
# Policy update
pi_optimizer.zero_grad()
pi_loss.backward()
pi_optimizer.step()
logger.store(LossPi=pi_loss)
# Polyak averaging for target variables
# Credits: https://github.com/ghliu/pytorch-ddpg/blob/master/util.py
params = zip(actor_critic_target.parameters(),
actor_critic_main.parameters())
for ac_target, ac_main in params:
ac_target.data.copy_(ac_main.data * (1.0 - polyak) +
ac_target.data * polyak)
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, actor_critic_main, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('QVals', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def td3(env_fn,
expert=None,
policy_path=None,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=500,
epochs=1000,
replay_size=int(5e3),
gamma=0.99,
polyak=0.995,
pi_lr=1e-4,
q_lr=1e-4,
batch_size=64,
start_epochs=500,
dagger_epochs=500,
pretrain_epochs=50,
dagger_noise=0.02,
act_noise=0.02,
target_noise=0.02,
noise_clip=0.5,
policy_delay=2,
max_ep_len=500,
logger_kwargs=dict(),
save_freq=50,
UPDATE_STEP=10):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Deterministically computes actions
| from policy given states.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to TD3.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
pi_lr (float): Learning rate for policy.
q_lr (float): Learning rate for Q-networks.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
act_noise (float): Stddev for Gaussian exploration noise added to
policy at training time. (At test time, no noise is added.)
target_noise (float): Stddev for smoothing noise added to target
policy.
noise_clip (float): Limit for absolute value of target policy
smoothing noise.
policy_delay (int): Policy will only be updated once every
policy_delay times for each update of the Q-networks.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
test_logger_kwargs = dict()
test_logger_kwargs['output_dir'] = osp.join(logger_kwargs['output_dir'],
"test")
test_logger_kwargs['exp_name'] = logger_kwargs['exp_name']
test_logger = EpochLogger(**test_logger_kwargs)
# test_logger_kwargs = dict()
# test_logger_kwargs['output_dir'] = osp.join(logger_kwargs['output_dir'], "test")
# test_logger_kwargs['exp_name'] = logger_kwargs['exp_name']
# test_logger = EpochLogger(**test_logger_kwargs)
# pretrain_logger_kwargs = dict()
# pretrain_logger_kwargs['output_dir'] = osp.join(logger_kwargs['output_dir'], "pretrain")
# pretrain_logger_kwargs['exp_name'] = logger_kwargs['exp_name']
# pretrain_logger = EpochLogger(**pretrain_logger_kwargs)
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, do not assumes all dimensions share the same bound!
act_limit = env.action_space.high / 2
act_high_limit = env.action_space.high
act_low_limit = env.action_space.low
act_noise_limit = act_noise * act_limit
sess = tf.Session()
if policy_path is None:
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(
obs_dim, act_dim, obs_dim, None, None)
tfa_ph = core.placeholder(act_dim)
# Main outputs from computation graph
with tf.variable_scope('main'):
pi, q1, q2, q1_pi = actor_critic(x_ph, a_ph, **ac_kwargs)
# Target policy network
with tf.variable_scope('target'):
pi_targ, _, _, _ = actor_critic(x2_ph, a_ph, **ac_kwargs)
# Target Q networks
with tf.variable_scope('target', reuse=True):
# Target policy smoothing, by adding clipped noise to target actions
epsilon = tf.random_normal(tf.shape(pi_targ), stddev=target_noise)
epsilon = tf.clip_by_value(epsilon, -noise_clip, noise_clip)
a2 = pi_targ + epsilon
a2 = tf.clip_by_value(a2, act_low_limit, act_high_limit)
# Target Q-values, using action from target policy
_, q1_targ, q2_targ, _ = actor_critic(x2_ph, a2, **ac_kwargs)
else:
# sess = tf.Session()
model = restore_tf_graph(sess, osp.join(policy_path, 'simple_save'))
x_ph, a_ph, x2_ph, r_ph, d_ph = model['x_ph'], model['a_ph'], model[
'x2_ph'], model['r_ph'], model['d_ph']
pi, q1, q2, q1_pi = model['pi'], model['q1'], model['q2'], model[
'q1_pi']
pi_targ, q1_targ, q2_targ = model['pi_targ'], model['q1_targ'], model[
'q2_targ']
tfa_ph = core.placeholder(act_dim)
dagger_epochs = 0
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
dagger_replay_buffer = DaggerReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables
var_counts = tuple(
core.count_vars(scope)
for scope in ['main/pi', 'main/q1', 'main/q2', 'main'])
print(
'\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d, \t total: %d\n'
% var_counts)
if policy_path is None:
# Bellman backup for Q functions, using Clipped Double-Q targets
min_q_targ = tf.minimum(q1_targ, q2_targ)
backup = tf.stop_gradient(r_ph + gamma * (1 - d_ph) * min_q_targ)
# dagger loss
dagger_pi_loss = tf.reduce_mean(tf.square(pi - tfa_ph))
# TD3 losses
pi_loss = -tf.reduce_mean(q1_pi)
q1_loss = tf.reduce_mean((q1 - backup)**2)
q2_loss = tf.reduce_mean((q2 - backup)**2)
q_loss = tf.add(q1_loss, q2_loss)
pi_loss = tf.identity(pi_loss, name="pi_loss")
q1_loss = tf.identity(q1_loss, name="q1_loss")
q2_loss = tf.identity(q2_loss, name="q2_loss")
q_loss = tf.identity(q_loss, name="q_loss")
# Separate train ops for pi, q
dagger_pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr)
train_dagger_pi_op = dagger_pi_optimizer.minimize(
dagger_pi_loss,
var_list=get_vars('main/pi'),
name='train_dagger_pi_op')
train_pi_op = pi_optimizer.minimize(pi_loss,
var_list=get_vars('main/pi'),
name='train_pi_op')
train_q_op = q_optimizer.minimize(q_loss,
var_list=get_vars('main/q'),
name='train_q_op')
# Polyak averaging for target variables
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess.run(tf.global_variables_initializer())
else:
graph = tf.get_default_graph()
# opts = graph.get_operations()
# print (opts)
pi_loss = model['pi_loss']
q1_loss = model['q1_loss']
q2_loss = model['q2_loss']
q_loss = model['q_loss']
train_q_op = graph.get_operation_by_name('train_q_op')
train_pi_op = graph.get_operation_by_name('train_pi_op')
# target_update = graph.get_operation_by_name('target_update')
# target_init = graph.get_operation_by_name('target_init')
# Polyak averaging for target variables
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# sess = tf.Session()
# sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x_ph': x_ph, 'a_ph': a_ph, 'x2_ph': x2_ph, 'r_ph': r_ph, 'd_ph': d_ph}, \
outputs={'pi': pi, 'q1': q1, 'q2': q2, 'q1_pi': q1_pi, 'pi_targ': pi_targ, 'q1_targ': q1_targ, 'q2_targ': q2_targ, \
'pi_loss': pi_loss, 'q1_loss': q1_loss, 'q2_loss': q2_loss, 'q_loss': q_loss})
def get_action(o, noise_scale):
a = sess.run(pi, feed_dict={x_ph: o.reshape(1, -1)})[0]
# todo: add act_limit scale noise
a += noise_scale * np.random.randn(act_dim)
return np.clip(a, act_low_limit, act_high_limit)
def choose_action(s, add_noise=False):
s = s[np.newaxis, :]
a = sess.run(pi, {x_ph: s})[0]
if add_noise:
noise = dagger_noise * act_high_limit * np.random.normal(
size=a.shape)
a = a + noise
return np.clip(a, act_low_limit, act_high_limit)
def test_agent(n=81, test_num=1):
n = env.unwrapped._set_test_mode(True)
con_flag = False
for j in range(n):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, info = env.step(choose_action(np.array(o), 0))
ep_ret += r
ep_len += 1
if d:
test_logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_logger.store(arrive_des=info['arrive_des'])
test_logger.store(
arrive_des_appro=info['arrive_des_appro'])
if not info['out_of_range']:
test_logger.store(converge_dis=info['converge_dis'])
con_flag = True
test_logger.store(out_of_range=info['out_of_range'])
# print(info)
# test_logger.dump_tabular()
# time.sleep(10)
if not con_flag:
test_logger.store(converge_dis=10000)
env.unwrapped._set_test_mode(False)
start_time = time.time()
env.unwrapped._set_test_mode(False)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
test_num = 0
total_env_t = 0
print(colorize("begin dagger training", 'green', bold=True))
# Main loop for dagger pretrain
for epoch in range(1, dagger_epochs + 1, 1):
obs, acs, rewards = [], [], []
# number of timesteps
for t in range(steps_per_epoch):
# action = env.action_space.sample()
# action = ppo.choose_action(np.array(observation))
obs.append(o)
ref_action = call_ref_controller(env, expert)
if (epoch < pretrain_epochs):
action = ref_action
else:
action = choose_action(np.array(o), True)
o2, r, d, info = env.step(action)
ep_ret += r
ep_len += 1
total_env_t += 1
acs.append(ref_action)
rewards.append(r)
# Store experience to replay buffer
replay_buffer.store(o, action, r, o2, d)
o = o2
if (t == steps_per_epoch - 1):
# print ("reached the end")
d = True
if d:
# collected data to replaybuffer
max_step = len(np.array(rewards))
q = [
np.sum(
np.power(gamma, np.arange(max_step - t)) * rewards[t:])
for t in range(max_step)
]
dagger_replay_buffer.stores(obs, acs, rewards, q)
# update policy
for _ in range(int(max_step / 5)):
batch = dagger_replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'], tfa_ph: batch['acts']}
q_step_ops = [dagger_pi_loss, train_dagger_pi_op]
for j in range(UPDATE_STEP):
outs = sess.run(q_step_ops, feed_dict)
logger.store(LossPi=outs[0])
# train q function
for j in range(int(max_step / 5)):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done']
}
q_step_ops = [q_loss, q1, q2, train_q_op]
# for _ in range(UPDATE_STEP):
outs = sess.run(q_step_ops, feed_dict)
logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2])
if j % policy_delay == 0:
# Delayed target update
outs = sess.run([target_update], feed_dict)
# logger.store(LossPi=outs[0])
# logger.store(LossQ=1000000, Q1Vals=1000000, Q2Vals=1000000)
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
break
# End of epoch wrap-up
if epoch > 0 and (epoch % save_freq == 0) or (epoch == dagger_epochs):
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
# Log info about epoch
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
sess.run(target_init)
print(colorize("begin td3 training", 'green', bold=True))
# Main loop: collect experience in env and update/log each epoch
# total_env_t = 0
for epoch in range(1, epochs + 1, 1):
# End of epoch wrap-up
if epoch > 0 and (epoch % save_freq == 0) or (epoch == epochs):
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
# Log info about epoch
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy (with some noise, via act_noise).
"""
# o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
for t in range(steps_per_epoch):
if epoch > start_epochs:
a = get_action(np.array(o), act_noise_limit)
else:
a = env.action_space.sample()
# ref_action = call_ref_controller(env, expert)
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
total_env_t += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
# d = False if ep_len==max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if (t == steps_per_epoch - 1):
# print ("reached the end")
d = True
if d:
"""
Perform all TD3 updates at the end of the trajectory
(in accordance with source code of TD3 published by
original authors).
"""
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done']
}
q_step_ops = [q_loss, q1, q2, train_q_op]
# for _ in range(UPDATE_STEP):
outs = sess.run(q_step_ops, feed_dict)
logger.store(LossQ=outs[0], Q1Vals=outs[1], Q2Vals=outs[2])
if j % policy_delay == 0:
# Delayed policy update
outs = sess.run([pi_loss, train_pi_op, target_update],
feed_dict)
logger.store(LossPi=outs[0])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
break
def acdf(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
pi_epochs=100,
vf_epochs=50,
gamma=0.99,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=1e-3,
train_pi_iters=80,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10,
demo_file=""):
# Special function to avoid certain slowdowns from PyTorch + MPI combo.
setup_pytorch_for_mpi()
# Set up logger and save configuration
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Random seed
seed += 10000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
# Instantiate environment
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# demo environment
demo_env = DemoGymEnv(demo_file=demo_file, seed=seed)
demo_env.check_env(env)
# Create actor-critic module
ac = actor_critic(env.observation_space, env.action_space,
**ac_kwargs).to(device)
# Sync params across processes
sync_params(ac)
# Count variables
var_counts = tuple(
core.count_vars(module) for module in [ac.pi, ac.v, ac.v_pi])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d and v_pi: %d\n' %
var_counts)
# Set up experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = ACDFBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Set up function for computing PPO policy loss
def compute_loss_pi(data):
obs, act, adv, logp_old = Variable(data['obs']), Variable(
data['act']), Variable(data['adv']), Variable(data['logp'])
# Policy loss
pi, logp = ac.pi(obs, act)
ratio = torch.exp(logp - logp_old)
clip_adv = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio) * adv
loss_pi = -(torch.min(ratio * adv, clip_adv)).mean()
# Useful extra info
approx_kl = (logp_old - logp).mean().item()
ent = pi.entropy().mean().item()
clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio)
clipfrac = torch.as_tensor(clipped, dtype=torch.float32,
device=device).mean().item()
pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac)
return loss_pi, pi_info
# Set up function for computing value loss
def compute_loss_v(data):
obs, ret = Variable(data['obs']), Variable(data['ret'])
return ((ac.v(obs) - ret)**2).mean()
def compute_loss_v_pi(data):
obs, ret = Variable(data['obs']), Variable(data['ret'])
return ((ac.v_pi(obs) - ret)**2).mean()
# Set up optimizers for policy and value function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr)
vf_pi_optimizer = Adam(ac.v_pi.parameters(), lr=vf_lr)
# Set up model savingF
logger.setup_pytorch_saver(ac)
def update():
data = buf.get()
pi_l_old, pi_info_old = compute_loss_pi(data)
pi_l_old = pi_l_old.item()
v_l_old = compute_loss_v(data).item()
# Train policy with multiple steps of gradient descent
for i in range(train_pi_iters):
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
kl = mpi_avg(pi_info['kl'])
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
loss_pi.backward()
mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
logger.store(StopIter=i)
# Value function learning
for i in range(train_v_iters):
vf_optimizer.zero_grad()
loss_v = compute_loss_v(data)
loss_v.backward()
mpi_avg_grads(ac.v) # average grads across MPI processes
vf_optimizer.step()
# Log changes from update
kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf']
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(loss_pi.item() - pi_l_old),
DeltaLossV=(loss_v.item() - v_l_old))
def demo_update():
data = buf.get()
pi_l_old, pi_info_old = compute_loss_pi(data)
pi_l_old = pi_l_old.item()
v_l_old = compute_loss_v_pi(data).item()
for i in range(train_pi_iters):
pi_optimizer.zero_grad()
loss_pi, pi_info = compute_loss_pi(data)
kl = mpi_avg(pi_info['kl'])
if kl > 1.5 * target_kl:
# logger.log('Early stopping at step %d due to reaching max kl.' % i)
break
loss_pi.backward()
mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
# logger.store(StopIter=i)
for i in range(train_v_iters):
vf_pi_optimizer.zero_grad()
loss_v = compute_loss_v_pi(data)
loss_v.backward()
mpi_avg_grads(ac.v_pi)
vf_pi_optimizer.step()
print("Pi loss: {}".format(pi_l_old))
# kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf']
# logger.store(LossPi=pi_l_old, LossV=v_l_old,
# KL=kl, Entropy=ent, ClipFrac=cf,
# DeltaLossPi=(loss_pi.item() - pi_l_old),
# DeltaLossV=(loss_v.item() - v_l_old))
def update_vf():
data = buf.get()
v_l_old = compute_loss_v(data).item()
print("Loss for Value function: {}".format(v_l_old))
for i in range(train_v_iters):
vf_optimizer.zero_grad()
loss_v = compute_loss_v(data)
loss_v.backward()
mpi_avg_grads(ac.v)
vf_optimizer.step()
# pretraining epochs
# pi_epochs, vf_epochs = 100, 50
# demonstration training: main loop, for policy network
o, ep_ret, ep_len = demo_env.reset(), 0, 0
start_time = time.time()
for epoch in range(pi_epochs):
for t in range(local_steps_per_epoch):
a, v, logp_a, m, std = ac.pretrain_step(
torch.as_tensor(o, dtype=torch.float32, device=device))
next_o, r, d, _ = demo_env.step(a, std)
ep_ret += r
ep_len += 1
buf.store(o, a, r, v, logp_a, std=std)
# logger.store(VVals=v)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len,
flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
_, v, _, _, _ = ac.pretrain_step(
torch.as_tensor(o, dtype=torch.float32, device=device))
else:
v = 0
# if terminal:
# # only save EpRet / EpLen if trajectory finished
# # logger.store(EpRet=ep_ret, EpLen=ep_len)
buf.finish_path(v)
o, ep_ret, ep_len = demo_env.reset(), 0, 0
demo_update()
# # Log info about epoch
# logger.log_tabular('Epoch', epoch)
# logger.log_tabular('EpRet', with_min_and_max=True)
# logger.log_tabular('EpLen', average_only=True)
# logger.log_tabular('VVals', with_min_and_max=True)
# logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
# logger.log_tabular('LossPi', average_only=True)
# logger.log_tabular('LossV', average_only=True)
# logger.log_tabular('DeltaLossPi', average_only=True)
# logger.log_tabular('DeltaLossV', average_only=True)
# logger.log_tabular('Entropy', average_only=True)
# logger.log_tabular('KL', average_only=True)
# logger.log_tabular('ClipFrac', average_only=True)
# logger.log_tabular('StopIter', average_only=True)
# logger.log_tabular('Time', time.time() - start_time)
# logger.dump_tabular()
# for the value function pre-training
o, ep_ret, ep_len = demo_env.reset(), 0, 0
start_time = time.time()
for epoch in range(vf_epochs):
for t in range(local_steps_per_epoch):
next_o, r, d, _, a = demo_env.free_step()
v = ac.v(torch.as_tensor(o, dtype=torch.float32,
device=device)).cpu().detach().numpy()
ep_ret += r
ep_len += 1
buf.store(o, a, r, v, 1)
# logger.store(VVals=v)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len,
flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
v = ac.v(
torch.as_tensor(o, dtype=torch.float32,
device=device)).cpu().detach().numpy()
else:
v = 0
buf.finish_path(v)
o, ep_ret, ep_len = demo_env.reset(), 0, 0
print("Pretraining for value function at Epoch: {}".format(epoch))
update_vf()
# Prepare for interaction with environment
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
buf = ACDFBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v, logp = ac.step(
torch.as_tensor(o, dtype=torch.float32, device=device))
next_o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# save and log
buf.store(o, a, r, v, logp)
logger.store(VVals=v)
# Update obs (critical!)
o = next_o
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
if epoch_ended and not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len,
flush=True)
# if trajectory didn't reach terminal state, bootstrap value target
if timeout or epoch_ended:
_, v, _ = ac.step(
torch.as_tensor(o, dtype=torch.float32, device=device))
else:
v = 0
buf.finish_path(v)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform PPO update!
update()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sac(env,
logger_kwargs=dict(),
network_params=dict(),
rl_params=dict(),
resume_training=False,
resume_params=dict()):
logger = EpochLogger(**logger_kwargs)
if not resume_training:
save_vars = locals().copy()
save_vars.pop('env')
logger.save_config(save_vars)
# ==== control params ====
seed = rl_params['seed']
epochs = rl_params['epochs']
steps_per_epoch = rl_params['steps_per_epoch']
replay_size = rl_params['replay_size']
update_freq = rl_params['update_freq']
n_updates = rl_params['n_updates']
batch_size = rl_params['batch_size']
start_steps = rl_params['start_steps']
max_ep_len = rl_params['max_ep_len']
num_tests = rl_params['num_tests']
save_freq = rl_params['save_freq']
# ==== rl params ====
use_HER = rl_params['use_HER']
use_prev_a = rl_params['use_prev_a']
gamma = rl_params['gamma']
polyak = rl_params['polyak']
act_lr = rl_params['act_lr']
crit_lr = rl_params['crit_lr']
alph_lr = rl_params['alph_lr']
# ==== exploration params ====
alpha = rl_params['alpha']
target_entropy = rl_params['target_entropy']
if not resume_training:
sess = tf.compat.v1.Session(config=tf_config)
# set seeding (still not perfectly deterministic)
tf.set_random_seed(seed)
np.random.seed(seed)
env.seed(seed)
env.action_space.np_random.seed(seed)
# get required gym spaces
obs = env.observation_space
act = env.action_space
# get the obs size after resize of raw image
obs_dim = network_params['input_dims']
act_dim = env.action_space.shape[0]
act_low = env.action_space.low[0]
act_high = env.action_space.high[0]
goal_dim = len(env.goal_list)
if not resume_training:
# init a state buffer for storing last m states
train_state_buffer = StateBuffer(m=obs_dim[2])
test_state_buffer = StateBuffer(m=obs_dim[2])
# Experience buffer
replay_buffer = ContReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
goal_dim=goal_dim,
size=replay_size)
# Inputs to computation graph
x_ph, a_ph, prev_a_ph, x2_ph, r_ph, d_ph, g_ph = placeholders(
obs_dim, act_dim, act_dim, obs_dim, None, None, goal_dim)
# alpha Params
if target_entropy == 'auto':
target_entropy = tf.cast(-act_dim, tf.float32)
else:
target_entropy = tf.cast(target_entropy, tf.float32)
log_alpha = tf.get_variable('log_alpha',
dtype=tf.float32,
initializer=0.0)
if alpha == 'auto': # auto tune alpha
alpha = tf.exp(log_alpha)
else: # fixed alpha
alpha = tf.get_variable('alpha',
dtype=tf.float32,
initializer=alpha)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi, q1_a, q2_a = create_rl_networks(
x_ph, a_ph, use_prev_a, prev_a_ph, g_ph, act_high,
network_params)
with tf.variable_scope('main', reuse=True):
# compose q with pi, for pi-learning
_, _, _, q1_pi, q2_pi = create_rl_networks(x_ph, pi, use_prev_a,
prev_a_ph, g_ph,
act_high,
network_params)
# get actions and log probs of actions for next states, for Q-learning
_, pi_next, logp_pi_next, _, _ = create_rl_networks(
x2_ph, a_ph, use_prev_a, prev_a_ph, g_ph, act_high,
network_params)
# Target networks
with tf.variable_scope('target'):
_, _, _, q1_pi_targ, q2_pi_targ = create_rl_networks(
x2_ph, pi_next, use_prev_a, a_ph, g_ph, act_high,
network_params)
var_counts = tuple(
count_vars(scope) for scope in
['log_alpha', 'main/pi', 'main/q1', 'main/q2', 'main'])
print("""\nNumber of parameters:
alpha: %d,
pi: %d,
q1: %d,
q2: %d,
total: %d\n""" % var_counts)
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
min_q_pi_targ = tf.minimum(q1_pi_targ, q2_pi_targ)
# Targets for Q and V regression
q_backup = tf.stop_gradient(r_ph + gamma * (1 - d_ph) *
(min_q_pi_targ - alpha * logp_pi_next))
# critic losses
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1_a)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2_a)**2)
value_loss = q1_loss + q2_loss
# Soft actor losses
pi_loss = tf.reduce_mean(alpha * logp_pi - min_q_pi)
# alpha loss for temperature parameter
alpha_backup = tf.stop_gradient(logp_pi + target_entropy)
alpha_loss = -tf.reduce_mean((log_alpha * alpha_backup))
# Policy train op
# (has to be separate from value train op, because q1_pi appears in pi_loss)
pi_optimizer = tf.train.AdamOptimizer(learning_rate=act_lr,
epsilon=1e-04)
train_pi_op = pi_optimizer.minimize(pi_loss,
var_list=get_vars('main/pi'),
name='train_pi_op')
# Value train op
value_optimizer = tf.train.AdamOptimizer(learning_rate=crit_lr,
epsilon=1e-04)
with tf.control_dependencies([train_pi_op]):
train_value_op = value_optimizer.minimize(
value_loss, var_list=get_vars('main/q'), name='train_value_op')
# Alpha train op
alpha_optimizer = tf.train.AdamOptimizer(learning_rate=alph_lr,
epsilon=1e-04)
with tf.control_dependencies([train_value_op]):
train_alpha_op = alpha_optimizer.minimize(
alpha_loss,
var_list=get_vars('log_alpha'),
name='train_alpha_op')
# Polyak averaging for target variables
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
],
name='target_update')
# Initializing targets to match main variables
target_init = tf.group([
tf.compat.v1.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(target_init)
else:
# if resuming define all the ph and outputs from saved model
# inputs
x_ph = resume_params['model']['x_ph']
a_ph = resume_params['model']['a_ph']
prev_a_ph = resume_params['model']['prev_a_ph']
x2_ph = resume_params['model']['x2_ph']
r_ph = resume_params['model']['r_ph']
d_ph = resume_params['model']['d_ph']
g_ph = resume_params['model']['g_ph']
# outputs
mu = resume_params['model']['mu']
pi = resume_params['model']['pi']
pi_loss = resume_params['model']['pi_loss']
q1_loss = resume_params['model']['q1_loss']
q2_loss = resume_params['model']['q2_loss']
q1_a = resume_params['model']['q1_a']
q2_a = resume_params['model']['q2_a']
logp_pi = resume_params['model']['logp_pi']
target_entropy = resume_params['model']['target_entropy']
alpha_loss = resume_params['model']['alpha_loss']
alpha = resume_params['model']['alpha']
# buffers
replay_buffer = resume_params['resume_state']['replay_buffer']
train_state_buffer = resume_params['resume_state'][
'train_state_buffer']
test_state_buffer = resume_params['resume_state']['test_state_buffer']
# get needed operations from graph by name (trouble saving these)
train_pi_op = tf.get_default_graph().get_operation_by_name(
"train_pi_op")
train_value_op = tf.get_default_graph().get_operation_by_name(
"train_value_op")
train_alpha_op = tf.get_default_graph().get_operation_by_name(
"train_alpha_op")
target_update = tf.get_default_graph().get_operation_by_name(
"target_update")
sess = resume_params['sess']
# All ops to call during one training step
step_ops = [
pi_loss, q1_loss, q2_loss, q1_a, q2_a, logp_pi, target_entropy,
alpha_loss, alpha, train_pi_op, train_value_op, train_alpha_op,
target_update
]
# Setup model saving
if save_freq is not None:
logger.setup_tf_saver(sess,
inputs={
'x_ph': x_ph,
'a_ph': a_ph,
'prev_a_ph': prev_a_ph,
'x2_ph': x2_ph,
'r_ph': r_ph,
'd_ph': d_ph,
'g_ph': g_ph
},
outputs={
'mu': mu,
'pi': pi,
'pi_loss': pi_loss,
'q1_loss': q1_loss,
'q2_loss': q2_loss,
'q1_a': q1_a,
'q2_a': q2_a,
'logp_pi': logp_pi,
'target_entropy': target_entropy,
'alpha_loss': alpha_loss,
'alpha': alpha
})
def get_action(state, one_hot_goal, prev_a, deterministic=False):
state = state.astype('float32') / 255.
act_op = mu if deterministic else pi
a = sess.run(act_op,
feed_dict={
x_ph: [state],
g_ph: [one_hot_goal],
prev_a_ph: [prev_a]
})[0]
return a
def reset(state_buffer):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
o = process_image_observation(o, obs_dim)
r = process_reward(r)
state = state_buffer.init_state(init_obs=o)
prev_a = np.zeros(act_dim)
# new random goal when the env is reset
goal_id = np.random.randint(goal_dim)
one_hot_goal = np.eye(goal_dim)[goal_id]
goal = env.goal_list[goal_id]
env.goal_button = goal
# print('Goal Button: {}'.format(goal))
return o, r, d, ep_ret, ep_len, state, one_hot_goal, prev_a
def test_agent(n=1):
print('Testing...')
for j in range(n):
test_o, test_r, test_d, test_ep_ret, test_ep_len, test_state, test_one_hot_goal, test_prev_a = reset(
test_state_buffer)
while not (test_d or (test_ep_len == max_ep_len)):
test_a = get_action(test_state, test_one_hot_goal, test_prev_a,
True)
test_o, test_r, test_d, _ = env.step(test_a)
test_o = process_image_observation(test_o, obs_dim)
test_r = process_reward(test_r)
test_state = test_state_buffer.append_state(test_o)
test_ep_ret += test_r
test_ep_len += 1
test_prev_a = test_a
logger.store(TestEpRet=test_ep_ret, TestEpLen=test_ep_len)
# ================== Main training Loop ==================
if not resume_training:
start_time = time.time()
o, r, d, ep_ret, ep_len, state, one_hot_goal, prev_a = reset(
train_state_buffer)
total_steps = steps_per_epoch * epochs
resume_t = 0
# array for storing states used with HER
if use_HER:
HER_buffer = ContHERBuffer(obs_dim=obs_dim,
act_dim=act_dim,
goal_dim=goal_dim,
size=max_ep_len)
# resuming training
else:
start_time = time.time()
total_steps = steps_per_epoch * (epochs +
resume_params['additional_epochs'])
HER_buffer = resume_params['resume_state']['HER_buffer']
resume_t = resume_params['resume_state']['resume_t']
o, r, d, ep_ret, ep_len, state, one_hot_goal, prev_a = resume_params[
'resume_state']['rl_state']
# reset the environment to the state set before saving
env.set_env_state(resume_params['resume_state']['env_state'])
# Main loop: collect experience in env and update/log each epoch
for t in range(resume_t, total_steps):
if t > start_steps:
a = get_action(state, one_hot_goal, prev_a, False)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
o2 = process_image_observation(o2, obs_dim) # thresholding done in env
r = process_reward(r)
next_state = train_state_buffer.append_state(o2)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
# if life is lost then store done as true true
replay_buffer.store(state, a, prev_a, r, next_state, d, one_hot_goal)
# append to HER buffer
if use_HER:
HER_buffer.store(state, a, prev_a, r, next_state, d, one_hot_goal)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
state = next_state
prev_a = a
# store additional states in replay buffer where the goal
# is given by the final state, if the final state was incorrect
if use_HER:
if d and (ep_len != max_ep_len):
# get actual goal achieved
achieved_goal = np.eye(goal_dim)[env.goal_list.index(
env.latest_button)]
# if an incorrect goal was reached
if (achieved_goal != one_hot_goal).any():
for j in range(ep_len):
# pull data from HER buffer
sample = HER_buffer.sample(j)
# change this to calc_rew function in env
if j == ep_len - 1:
new_rew = env.max_rew
else:
new_rew = sample['rews']
# add to replay buffer
replay_buffer.store(sample['obs1'], sample['acts'],
sample['prev_acts'], new_rew,
sample['obs2'], sample['done'],
achieved_goal)
# do a single update
if t > 0 and t % update_freq == 0:
for i in range(n_updates):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
prev_a_ph: batch['prev_acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
g_ph: batch['goal']
}
outs = sess.run(step_ops, feed_dict)
logger.store(LossPi=outs[0],
LossQ1=outs[1],
LossQ2=outs[2],
Q1Vals=outs[3],
Q2Vals=outs[4],
LogPPi=outs[5],
TargEntropy=outs[6],
LossAlpha=outs[7],
Alpha=outs[8])
if d or (ep_len == max_ep_len):
# store episode values
logger.store(EpRet=ep_ret, EpLen=ep_len)
# reset the environment
o, r, d, ep_ret, ep_len, state, one_hot_goal, prev_a = reset(
train_state_buffer)
if use_HER:
# reset HER buffer
HER_buffer.reset()
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# save everything neccessary for restarting training from current position
env_state = env.get_env_state()
# Save model
if save_freq is not None:
if (epoch % save_freq == 0) or (epoch == epochs - 1):
print('Saving...')
rl_state = [
o, r, d, ep_ret, ep_len, state, one_hot_goal, prev_a
]
logger.save_state(
state_dict={
'env_state': env_state,
'replay_buffer': replay_buffer,
'train_state_buffer': train_state_buffer,
'test_state_buffer': test_state_buffer,
'HER_buffer': HER_buffer,
'resume_t': t + 1,
'rl_state': rl_state
})
# Test the performance of the deterministic version of the agent. (resets the env)
test_agent(n=num_tests)
# set params for resuming training
env.set_env_state(env_state)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('LogPPi', average_only=True)
logger.log_tabular('TargEntropy', average_only=True)
logger.log_tabular('Alpha', average_only=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossAlpha', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
plot_progress(os.path.join(logger_kwargs['output_dir'], 'progress.txt'),
show_plot=False)
def ddpg(env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=5000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
pi_lr=1e-3,
q_lr=1e-3,
batch_size=100,
start_steps=10000,
act_noise=0.1,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Deterministically computes actions
| from policy given states.
``q`` (batch,) | Gives the current estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q_pi`` (batch,) | Gives the composition of ``q`` and
| ``pi`` for states in ``x_ph``:
| q(x, pi(x)).
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to DDPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
pi_lr (float): Learning rate for policy.
q_lr (float): Learning rate for Q-networks.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
act_noise (float): Stddev for Gaussian exploration noise added to
policy at training time. (At test time, no noise is added.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Action limit for clamping: critically, assumes all dimensions share the same bound!
act_limit = env.action_space.high[0]
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim,
obs_dim, None, None)
# Main outputs from computation graph
with tf.variable_scope('main'):
pi, q, q_pi = actor_critic(x_ph, a_ph, **ac_kwargs)
# Target networks
with tf.variable_scope('target'):
# Note that the action placeholder going to actor_critic here is
# irrelevant, because we only need q_targ(s, pi_targ(s)).
pi_targ, _, q_pi_targ = actor_critic(x2_ph, a_ph, **ac_kwargs)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables
var_counts = tuple(
core.count_vars(scope) for scope in ['main/pi', 'main/q', 'main'])
print('\nNumber of parameters: \t pi: %d, \t q: %d, \t total: %d\n' %
var_counts)
# Bellman backup for Q function
backup = tf.stop_gradient(r_ph + gamma * (1 - d_ph) * q_pi_targ)
# DDPG losses
pi_loss = -tf.reduce_mean(q_pi)
q_loss = tf.reduce_mean((q - backup)**2)
# Separate train ops for pi, q
pi_optimizer = tf.train.AdamOptimizer(learning_rate=pi_lr)
q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr)
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
train_q_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q'))
# Polyak averaging for target variables
target_update = tf.group([
tf.assign(v_targ, polyak * v_targ + (1 - polyak) * v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
# Initializing targets to match main variables
target_init = tf.group([
tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))
])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph
},
outputs={
'pi': pi,
'q': q
})
def get_action(o, noise_scale):
a = sess.run(pi, feed_dict={x_ph: o.reshape(1, -1)})
a += noise_scale * np.random.randn(act_dim)
return np.clip(a, -act_limit, act_limit)
def test_agent(n=10):
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, _ = test_env.step(get_action(o, 0))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy (with some noise, via act_noise).
"""
if t > start_steps:
a = get_action(o, act_noise)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
"""
Perform all DDPG updates at the end of the trajectory,
in accordance with tuning done by TD3 paper authors.
"""
for _ in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done']
}
# Q-learning update
outs = sess.run([q_loss, q, train_q_op], feed_dict)
logger.store(LossQ=outs[0], QVals=outs[1])
# Policy update
outs = sess.run([pi_loss, train_pi_op, target_update],
feed_dict)
logger.store(LossPi=outs[0])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('QVals', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sqn(env_fn, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
steps_per_epoch=5000, epochs=100, replay_size=int(1e6), gamma=0.99,
polyak=0.995, lr=1e-3, alpha=0.2, batch_size=100, start_steps=10000,
max_ep_len=1000, logger_kwargs=dict(), save_freq=1):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``mu`` (batch, act_dim) | Computes mean actions from policy
| given states.
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``. Critical: must be differentiable
| with respect to policy parameters all
| the way through action sampling.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
``q2_pi`` (batch,) | Gives the composition of ``q2`` and
| ``pi`` for states in ``x_ph``:
| q2(x, pi(x)).
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to SAC.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for policy/value/alpha learning).
alpha (float/'auto'): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.) / 'auto': alpha is automated.
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
# print(max_ep_len,type(max_ep_len))
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
tf.set_random_seed(seed)
np.random.seed(seed)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
obs_space = env.observation_space
act_dim = env.action_space.n
act_space = env.action_space
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders_from_space(obs_space, act_space, obs_space, None, None)
######
if alpha == 'auto':
# target_entropy = (-np.prod(env.action_space.n))
# target_entropy = (np.prod(env.action_space.n))/4/10
target_entropy = 0.15
log_alpha = tf.get_variable('log_alpha', dtype=tf.float32, initializer=0.0)
alpha = tf.exp(log_alpha)
######
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, _, q1, q2, q1_pi, q2_pi = actor_critic(x_ph, a_ph, alpha, **ac_kwargs)
# Target value network
with tf.variable_scope('target'):
_, _, logp_pi_, _, _,q1_pi_, q2_pi_= actor_critic(x2_ph, a_ph, alpha, **ac_kwargs)
# Experience buffer
if isinstance(act_space, Box):
a_dim = act_dim
elif isinstance(act_space, Discrete):
a_dim = 1
replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=a_dim, size=replay_size)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in
['main/pi', 'main/q1', 'main/q2', 'main'])
print(('\nNumber of parameters: \t pi: %d, \t' + \
'q1: %d, \t q2: %d, \t total: %d\n')%var_counts)
######
if isinstance(alpha,tf.Tensor):
alpha_loss = tf.reduce_mean(-log_alpha * tf.stop_gradient(logp_pi_ + target_entropy))
alpha_optimizer = tf.train.AdamOptimizer(learning_rate=lr, name='alpha_optimizer')
train_alpha_op = alpha_optimizer.minimize(loss=alpha_loss, var_list=[log_alpha])
######
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi_, q2_pi_)
# Targets for Q and V regression
v_backup = tf.stop_gradient(min_q_pi - alpha * logp_pi_) ############################## alpha=0
q_backup = r_ph + gamma*(1-d_ph)*v_backup
# Soft actor-critic losses
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2)**2)
value_loss = q1_loss + q2_loss
# # Policy train op
# # (has to be separate from value train op, because q1_pi appears in pi_loss)
# pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
# train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'))
# Value train op
# (control dep of train_pi_op because sess.run otherwise evaluates in nondeterministic order)
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
value_params = get_vars('main/q')
#with tf.control_dependencies([train_pi_op]):
train_value_op = value_optimizer.minimize(value_loss, var_list=value_params)
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([tf.assign(v_targ, polyak*v_targ + (1-polyak)*v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# All ops to call during one training step
if isinstance(alpha, Number):
step_ops = [q1_loss, q2_loss, q1, q2, logp_pi_, tf.identity(alpha),
train_value_op, target_update]
else:
step_ops = [q1_loss, q2_loss, q1, q2, logp_pi_, alpha,
train_value_op, target_update, train_alpha_op]
# Initializing targets to match main variables
target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph, 'a': a_ph},
outputs={'mu': mu, 'pi': pi, 'q1': q1, 'q2': q2})
def get_action(o, deterministic=False):
act_op = mu if deterministic else pi
return sess.run(act_op, feed_dict={x_ph: np.expand_dims(o, axis=0)})[0]
def test_agent(n=20): # n: number of tests
global sess, mu, pi, q1, q2, q1_pi, q2_pi
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not(d or (ep_len == max_ep_len)): # max_ep_len
# Take deterministic actions at test time
o, r, d, _ = test_env.step(get_action(o, True))
ep_ret += r
ep_len += 1
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
# o = env.reset() #####################
# o, r, d, ep_ret, ep_len = env.step(1)[0], 0, False, 0, 0 #####################
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
# Main loop: collect experience in env and update/log each epoch
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
# if t > start_steps and 100*t/total_steps > np.random.random(): # greedy, avoid falling into sub-optimum
if t > start_steps:
a = get_action(o)
else:
a = env.action_space.sample()
np.random.random()
# Step the env
o2, r, d, _ = env.step(a)
#print(a,o2)
# o2, r, _, d = env.step(a) #####################
# d = d['ale.lives'] < 5 #####################
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len==max_ep_len else d
# Store experience to replay buffer
replay_buffer.store(o, a, r, o2, d)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
# End of episode. Training (ep_len times).
if d or (ep_len == max_ep_len): # make sure: max_ep_len < steps_per_epoch
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
# step_ops = [q1_loss, q2_loss, q1, q2, logp_pi, alpha, train_pi_op, train_value_op, target_update]
outs = sess.run(step_ops, feed_dict)
logger.store(LossQ1=outs[0], LossQ2=outs[1],
Q1Vals=outs[2], Q2Vals=outs[3],
LogPi=outs[4], Alpha=outs[5])
#if d:
logger.store(EpRet=ep_ret, EpLen=ep_len)
# o = env.reset() #####################
# o, r, d, ep_ret, ep_len = env.step(1)[0], 0, False, 0, 0 #####################
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# End of epoch wrap-up
if t > 0 and t % steps_per_epoch == 0:
epoch = t // steps_per_epoch
# Save model
if (epoch % save_freq == 0) or (epoch == epochs-1):
logger.save_state({'env': env}, None)
# Test the performance of the deterministic version of the agent.
test_agent()
# logger.store(): store the data; logger.log_tabular(): log the data; logger.dump_tabular(): write the data
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Alpha',average_only=True)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
# logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
# logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
# logger.log_tabular('LossV', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
def a2c(env_fn,
model: IActorCritic,
seed=0,
num_cpu=1,
device=torch.device("cpu"),
epochs=1000,
steps_per_epoch=100,
episode_len_limit=None,
gamma=0.99,
use_gae=True,
tau=0.95,
max_grad_norm=0.5,
polyak=0.995,
learning_rate=1e-3,
value_loss_coef=0.5,
policy_loss_coef=1,
entropy_loss_coef=0.1,
save_every=100,
log_every=10,
logger_kwargs=dict(),
test_every=100,
num_test_episodes=5,
test_episode_len_limit=None,
deterministic=False,
save_freq=1,
solved_score=None,
):
use_MPI = num_cpu > 1
if use_MPI:
# Special function to avoid certain slowdowns from PyTorch + MPI combo.
mpi_pytorch.setup_pytorch_for_mpi()
else:
torch.set_num_threads(torch.get_num_threads())
# Set up logger and save configuration
logger = EpochLogger(**logger_kwargs)
config = locals()
del config['env_fn']
del config['model']
del config['logger']
logger.save_config(config)
test_logger_kwargs = deepcopy(logger_kwargs)
test_logger_kwargs['output_dir'] = pathlib.Path(test_logger_kwargs['output_dir']) / 'evaluation'
test_logger = EpochLogger(**test_logger_kwargs)
# Random seed
if use_MPI:
seed += 10000 * mpi_tools.proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
# Instantiate environment
env = env_fn()
test_env = env_fn()
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
# episode length limit
if episode_len_limit is None:
if env.unwrapped.spec and env.unwrapped.spec.max_episode_steps:
episode_len_limit = env.spec.max_episode_steps
else:
raise ValueError("Episode length limit must be specified")
if test_episode_len_limit is None:
test_episode_len_limit = episode_len_limit
# training model and target model
actor_critic = model
target_actor_critic = deepcopy(actor_critic)
if use_MPI:
# Sync params across processes
mpi_pytorch.sync_params(actor_critic)
mpi_pytorch.sync_params(target_actor_critic)
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in target_actor_critic.parameters():
p.requires_grad = False
# Utilize GPU
actor_critic.to(device)
target_actor_critic.to(device)
# Set up optimizers for policy and q-function
optimizer = Adam(actor_critic.parameters(), lr=learning_rate)
# Set up model saving
logger.setup_pytorch_saver(actor_critic, name='model')
def update(episode_buffer):
# Update
if episode_buffer.dones[-1]:
next_value = 0.0
else:
last_obs = episode_buffer.next_observations[-1]
last_obs_tensor = torch.tensor(last_obs, dtype=torch.float32).unsqueeze(0)
context = actor_critic.get_context()
next_value = target_actor_critic.predict_value(last_obs_tensor, context=context).cpu().item()
# Super critical!!
optimizer.zero_grad()
# Compute value and policy losses
loss, info = actor_critic.compute_loss(rewards=np.array(episode_buffer.rewards),
dones=np.array(episode_buffer.dones),
next_value=next_value,
discount_factor=gamma,
use_gae=use_gae,
tau=tau,
value_loss_coef=value_loss_coef,
policy_loss_coef=policy_loss_coef,
entropy_reg_coef=entropy_loss_coef)
loss.backward()
if use_MPI:
mpi_pytorch.mpi_avg_grads(actor_critic)
# Optimize
if max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(actor_critic.parameters(), max_grad_norm)
optimizer.step()
# Log losses and info
logger.store(**info)
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(actor_critic.parameters(), target_actor_critic.parameters()):
# NB: We use an in-place operations "mul_", "add_" to update target
# params, as opposed to "mul" and "add", which would make new tensors.
p_targ.data.mul_(polyak)
p_targ.data.add_((1 - polyak) * p.data)
if use_MPI:
mpi_pytorch.sync_params(target_actor_critic)
# Prepare for interaction with environment
start_time = time.time()
# Main loop: collect experience in env and update/log each epoch
total_steps = 0
# Reset env
obs = env.reset()
# Reset episode stats
episode_return = 0
episode_length = 0
for _ in range(5):
logger.store(EpRet=0, EpLen=0)
for epoch in range(1, epochs + 1):
actor_critic.reset_for_training()
epoch_history = EpisodeHistory()
for t in range(steps_per_epoch):
total_steps += 1
# Get action from the model
obs_tensor = torch.tensor(obs, dtype=torch.float32).unsqueeze(0)
action = actor_critic.step(obs_tensor)
# Step the env
obs2, reward, done, _ = env.step(action.detach().cpu().item())
episode_return += reward
episode_length += 1
# Store transition to history
epoch_history.store(observation=obs, action=action, reward=reward, done=done, next_observation=obs2)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
obs = obs2
# End of trajectory handling
if done or episode_length > episode_len_limit:
break
update(epoch_history)
# if done
if epoch_history.dones[-1]:
logger.store(EpRet=episode_return, EpLen=episode_length)
# Reset env
obs = env.reset()
actor_critic.reset()
# Reset episode stats
episode_return = 0
episode_length = 0
# End of epoch handling
if epoch % log_every == 0:
total_interactions = mpi_tools.mpi_sum(total_steps) if use_MPI else total_steps
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('Value', average_only=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossEntropy', average_only=True)
logger.log_tabular('TotalEnvInteracts', total_interactions)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
# Test agent
solved = False
if epoch % test_every == 0:
# Test the performance of the deterministic version of the agent.
context = actor_critic.get_context()
actor_critic.eval()
episode_info = evaluate_agent(env=test_env,
agent=actor_critic,
deterministic=deterministic,
num_episodes=num_test_episodes,
episode_len_limit=test_episode_len_limit,
render=False,
logger=test_logger)
actor_critic.train()
actor_critic.set_context(context)
if solved_score is not None:
solved = all(r >= solved_score for (t, r) in episode_info)
# Save model
if (epoch % save_every == 0) or (epoch == epochs) or solved:
logger.save_state({'env': env})
# Check environment is solved
if solved:
plog = lambda msg: logger.log(msg, color='green')
plog("=" * 40)
plog(f"ENVIRONMENT SOLVED!")
plog("=" * 40)
plog(f' TotalEnvInteracts {total_steps}')
plog(f' Time {time.time() - start_time}')
plog(f' Epoch {epoch}')
break
def sac(env_fn, expert=None, policy_path=None, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
steps_per_epoch=500, epochs=100000, replay_size=int(5e3), gamma=0.99,
dagger_noise=0.02, polyak=0.995, lr=1e-4, alpha=0.2, batch_size=64, dagger_epochs=200, pretrain_epochs=50,
max_ep_len=500, logger_kwargs=dict(), save_freq=50, update_steps=10):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``mu`` (batch, act_dim) | Computes mean actions from policy
| given states.
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``. Critical: must be differentiable
| with respect to policy parameters all
| the way through action sampling.
``q1`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q2`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``q1_pi`` (batch,) | Gives the composition of ``q1`` and
| ``pi`` for states in ``x_ph``:
| q1(x, pi(x)).
``q2_pi`` (batch,) | Gives the composition of ``q2`` and
| ``pi`` for states in ``x_ph``:
| q2(x, pi(x)).
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``.
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to SAC.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs to run and train agent.
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration.
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
test_logger_kwargs = dict()
test_logger_kwargs['output_dir'] = osp.join(logger_kwargs['output_dir'], "test")
test_logger_kwargs['exp_name'] = logger_kwargs['exp_name']
test_logger = EpochLogger(**test_logger_kwargs)
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
print(obs_dim)
print(act_dim)
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
act_high_limit = env.action_space.high
act_low_limit = env.action_space.low
sess = tf.Session()
if policy_path is None:
# Inputs to computation graph
x_ph, a_ph, x2_ph, r_ph, d_ph = core.placeholders(obs_dim, act_dim, obs_dim, None, None)
tfa_ph = core.placeholder(act_dim)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi, q1, q2, q1_pi, q2_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Target value network
with tf.variable_scope('target'):
_, _, _, _, _, _, _, v_targ = actor_critic(x2_ph, a_ph, **ac_kwargs)
# sess.run(tf.global_variables_initializer())
else:
# load pretrained model
model = restore_tf_graph(sess, osp.join(policy_path, 'simple_save'))
x_ph, a_ph, x2_ph, r_ph, d_ph = model['x_ph'], model['a_ph'], model['x2_ph'], model['r_ph'], model['d_ph']
mu, pi, logp_pi, q1, q2, q1_pi, q2_pi, v = model['mu'], model['pi'], model['logp_pi'], model['q1'], model['q2'], model['q1_pi'], model['q2_pi'], model['v']
# tfa_ph = core.placeholder(act_dim)
tfa_ph = model['tfa_ph']
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)
dagger_replay_buffer = DaggerReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in
['main/pi', 'main/q1', 'main/q2', 'main/v', 'main'])
print(('\nNumber of parameters: \t pi: %d, \t' + \
'q1: %d, \t q2: %d, \t v: %d, \t total: %d\n')%var_counts)
# print(obs_dim)
# print(act_dim)
# SAC objectives
if policy_path is None:
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
# Targets for Q and V regression
q_backup = tf.stop_gradient(r_ph + gamma*(1-d_ph)*v_targ)
v_backup = tf.stop_gradient(min_q_pi - alpha * logp_pi)
# Soft actor-critic losses
dagger_pi_loss = tf.reduce_mean(tf.square(mu-tfa_ph))
pi_loss = tf.reduce_mean(alpha * logp_pi - q1_pi)
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1)**2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2)**2)
v_loss = 0.5 * tf.reduce_mean((v_backup - v)**2)
value_loss = q1_loss + q2_loss + v_loss
# Policy train op
# (has to be separate from value train op, because q1_pi appears in pi_loss)
dagger_pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
train_dagger_pi_op = dagger_pi_optimizer.minimize(dagger_pi_loss, name='train_dagger_pi_op')
pi_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
train_pi_op = pi_optimizer.minimize(pi_loss, var_list=get_vars('main/pi'), name='train_pi_op')
# sess.run(tf.variables_initializer(pi_optimizer.variables()))
# Value train op
# (control dep of train_pi_op because sess.run otherwise evaluates in nondeterministic order)
value_optimizer = tf.train.AdamOptimizer(learning_rate=lr)
value_params = get_vars('main/q') + get_vars('main/v')
with tf.control_dependencies([train_pi_op]):
train_value_op = value_optimizer.minimize(value_loss, var_list=value_params, name='train_value_op')
# sess.run(tf.variables_initializer(value_optimizer.variables()))
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([tf.assign(v_targ, polyak*v_targ + (1-polyak)*v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# All ops to call during one training step
step_ops = [pi_loss, q1_loss, q2_loss, v_loss, q1, q2, v, logp_pi,
train_pi_op, train_value_op, target_update]
# Initializing targets to match main variables
target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
sess.run(tf.global_variables_initializer())
else:
graph = tf.get_default_graph()
dagger_pi_loss = model['dagger_pi_loss']
pi_loss = model['pi_loss']
q1_loss = model['q1_loss']
q2_loss = model['q2_loss']
v_loss = model['v_loss']
train_dagger_pi_op = graph.get_operation_by_name('train_dagger_pi_op')
train_value_op = graph.get_operation_by_name('train_value_op')
train_pi_op = graph.get_operation_by_name('train_pi_op')
# Polyak averaging for target variables
# (control flow because sess.run otherwise evaluates in nondeterministic order)
with tf.control_dependencies([train_value_op]):
target_update = tf.group([tf.assign(v_targ, polyak*v_targ + (1-polyak)*v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# All ops to call during one training step
step_ops = [pi_loss, q1_loss, q2_loss, v_loss, q1, q2, v, logp_pi,
train_pi_op, train_value_op, target_update]
# Initializing targets to match main variables
target_init = tf.group([tf.assign(v_targ, v_main)
for v_main, v_targ in zip(get_vars('main'), get_vars('target'))])
# sess = tf.Session()
# sess.run(tf.global_variables_initializer())
dagger_step_ops = [q1_loss, q2_loss, v_loss, q1, q2, v, logp_pi, train_value_op, target_update]
tf.summary.FileWriter("log/", sess.graph)
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x_ph': x_ph, 'a_ph': a_ph, 'tfa_ph': tfa_ph, 'x2_ph': x2_ph, 'r_ph': r_ph, 'd_ph': d_ph}, \
outputs={'mu': mu, 'pi': pi, 'v': v, 'logp_pi': logp_pi, 'q1': q1, 'q2': q2, 'q1_pi': q1_pi, 'q2_pi': q2_pi, \
'pi_loss': pi_loss, 'v_loss': v_loss, 'dagger_pi_loss': dagger_pi_loss, 'q1_loss': q1_loss, 'q2_loss': q2_loss})
def get_action(o, deterministic=False):
act_op = mu if deterministic else pi
a = sess.run(act_op, feed_dict={x_ph: o.reshape(1,-1)})[0]
return np.clip(a, act_low_limit, act_high_limit)
def choose_action(s, add_noise=False):
s = s[np.newaxis, :]
a = sess.run(mu, {x_ph: s})[0]
if add_noise:
noise = dagger_noise * act_high_limit * np.random.normal(size=a.shape)
a = a + noise
return np.clip(a, act_low_limit, act_high_limit)
def test_agent(n=81, test_num=1):
n = env.unwrapped._set_test_mode(True)
con_flag = False
for j in range(n):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not(d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
o, r, d, info = env.step(choose_action(np.array(o), 0))
ep_ret += r
ep_len += 1
if d:
test_logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_logger.store(arrive_des=info['arrive_des'])
test_logger.store(arrive_des_appro=info['arrive_des_appro'])
if not info['out_of_range']:
test_logger.store(converge_dis=info['converge_dis'])
con_flag = True
test_logger.store(out_of_range=info['out_of_range'])
# print(info)
# test_logger.dump_tabular()
# time.sleep(10)
if not con_flag:
test_logger.store(converge_dis=10000)
env.unwrapped._set_test_mode(False)
def ref_test_agent(n=81, test_num=1):
n = env.unwrapped._set_test_mode(True)
con_flag = False
for j in range(n):
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
while not(d or (ep_len == max_ep_len)):
# Take deterministic actions at test time (noise_scale=0)
a = call_ref_controller(env, expert)
o, r, d, info = env.step(a)
ep_ret += r
ep_len += 1
if d:
test_logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
test_logger.store(arrive_des=info['arrive_des'])
test_logger.store(arrive_des_appro=info['arrive_des_appro'])
if not info['out_of_range']:
test_logger.store(converge_dis=info['converge_dis'])
con_flag = True
test_logger.store(out_of_range=info['out_of_range'])
# print(info)
# test_logger.dump_tabular()
if not con_flag:
test_logger.store(converge_dis=10000)
env.unwrapped._set_test_mode(False)
# ref_test_agent(test_num = -1)
# test_logger.log_tabular('epoch', -1)
# test_logger.log_tabular('TestEpRet', average_only=True)
# test_logger.log_tabular('TestEpLen', average_only=True)
# test_logger.log_tabular('arrive_des', average_only=True)
# test_logger.log_tabular('arrive_des_appro', average_only=True)
# test_logger.log_tabular('converge_dis', average_only=True)
# test_logger.log_tabular('out_of_range', average_only=True)
# test_logger.dump_tabular()
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
episode_steps = 500
total_env_t = 0
test_num = 0
print(colorize("begin dagger training", 'green', bold=True))
for epoch in range(1, dagger_epochs + 1, 1):
# test policy
if epoch > 0 and (epoch % save_freq == 0) or (epoch == epochs):
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
test_logger.log_tabular('arrive_des_appro', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
# train policy
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
env.unwrapped._set_test_mode(False)
obs, acs, rewards = [], [], []
for t in range(steps_per_epoch):
obs.append(o)
ref_action = call_ref_controller(env, expert)
if(epoch < pretrain_epochs):
action = ref_action
else:
action = choose_action(np.array(o), True)
o2, r, d, _ = env.step(action)
o = o2
acs.append(ref_action)
rewards.append(r)
if (t == steps_per_epoch-1):
# print ("reached the end")
d = True
# Store experience to replay buffer
replay_buffer.store(o, action, r, o2, d)
ep_ret += r
ep_len += 1
total_env_t += 1
if d:
# Perform partical sac update!
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
outs = sess.run(dagger_step_ops, feed_dict)
logger.store(LossQ1=outs[0], LossQ2=outs[1],
LossV=outs[2], Q1Vals=outs[3], Q2Vals=outs[4],
VVals=outs[5], LogPi=outs[6])
# Perform dagger policy update
dagger_replay_buffer.stores(obs, acs, rewards)
for _ in range(int(ep_len/5)):
batch = dagger_replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'], tfa_ph: batch['acts']}
q_step_ops = [dagger_pi_loss, train_dagger_pi_op]
for j in range(10):
outs = sess.run(q_step_ops, feed_dict)
logger.store(LossPi = outs[0])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
break
# Main loop: collect experience in env and update/log each epoch
print(colorize("begin sac training", 'green', bold=True))
for epoch in range(1, epochs + 1, 1):
# test policy
if epoch > 0 and (epoch % save_freq == 0) or (epoch == epochs):
# Save model
logger.save_state({}, None)
# Test the performance of the deterministic version of the agent.
test_num += 1
test_agent(test_num=test_num)
test_logger.log_tabular('epoch', epoch)
test_logger.log_tabular('TestEpRet', average_only=True)
test_logger.log_tabular('TestEpLen', average_only=True)
test_logger.log_tabular('arrive_des', average_only=True)
# test_logger.log_tabular('arrive_des_appro', average_only=True)
test_logger.log_tabular('converge_dis', average_only=True)
test_logger.log_tabular('out_of_range', average_only=True)
test_logger.dump_tabular()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch+1)*steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
# logger.log_tabular('DeltaLossPi', average_only=True)
# logger.log_tabular('DeltaLossV', average_only=True)
# logger.log_tabular('Entropy', average_only=True)
# logger.log_tabular('KL', average_only=True)
# logger.log_tabular('ClipFrac', average_only=True)
# logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
# train policy
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
env.unwrapped._set_test_mode(False)
for t in range(steps_per_epoch):
a = get_action(np.array(o))
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
if (t == steps_per_epoch-1):
# print ("reached the end")
d = True
replay_buffer.store(o, a, r, o2, d)
o = o2
if d:
"""
Perform all SAC updates at the end of the trajectory.
This is a slight difference from the SAC specified in the
original paper.
"""
for j in range(ep_len):
batch = replay_buffer.sample_batch(batch_size)
feed_dict = {x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
d_ph: batch['done'],
}
outs = sess.run(step_ops, feed_dict)
logger.store(LossPi=outs[0], LossQ1=outs[1], LossQ2=outs[2],
LossV=outs[3], Q1Vals=outs[4], Q2Vals=outs[5],
VVals=outs[6], LogPi=outs[7])
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
def vpg(env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
pi_lr=3e-4,
vf_lr=1e-3,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=10,
custom_h=None,
do_checkpoint_eval=False,
env_name=None):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp`` (batch,) | Gives log probability, according to
| the policy, of taking actions ``a_ph``
| in states ``x_ph``.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``.
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``. (Critical: make sure
| to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to VPG.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# create logger for tensorboard
tb_logdir = "{}/tb_logs/".format(logger.output_dir)
tb_logger = Logger(log_dir=tb_logdir)
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
if custom_h is not None:
hidden_layers_str_list = custom_h.split('-')
hidden_layers_int_list = [int(h) for h in hidden_layers_str_list]
ac_kwargs['hidden_sizes'] = hidden_layers_int_list
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space,
env.action_space)
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
# Main outputs from computation graph
pi, logp, logp_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi]
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = VPGBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# VPG objectives
pi_loss = -tf.reduce_mean(logp * adv_ph)
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Info (useful to watch during learning)
approx_kl = tf.reduce_mean(
logp_old_ph -
logp) # a sample estimate for KL-divergence, easy to compute
approx_ent = tf.reduce_mean(
-logp) # a sample estimate for entropy, also easy to compute
# Optimizers
train_pi = MpiAdamOptimizer(learning_rate=pi_lr).minimize(pi_loss)
train_v = MpiAdamOptimizer(learning_rate=vf_lr).minimize(v_loss)
# create a tf session with GPU memory usage option to be allow_growth so that one program will not use up the
# whole GPU memory
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# log tf graph
tf.summary.FileWriter(tb_logdir, sess.graph)
# Sync params across processes
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'pi': pi, 'v': v})
# for saving the best models and performances during train and evaluate
best_eval_AverageEpRet = 0.0
best_eval_StdEpRet = 1.0e20
def update():
inputs = {k: v for k, v in zip(all_phs, buf.get())}
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Policy gradient step
sess.run(train_pi, feed_dict=inputs)
# Value function learning
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
pi_l_new, v_l_new, kl = sess.run([pi_loss, v_loss, approx_kl],
feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v_t, logp_t = sess.run(get_action_ops,
feed_dict={x_ph: o.reshape(1, -1)})
# save and log
buf.store(o, a, r, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(a[0])
ep_ret += r
ep_len += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t == local_steps_per_epoch - 1):
if not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = r if d else sess.run(
v, feed_dict={x_ph: o.reshape(1, -1)})
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
# Save a new model every save_freq and at the last epoch. Do not overwrite the previous save.
logger.save_state({'env': env}, epoch)
# Evaluate and save best model
if do_checkpoint_eval and epoch > 0:
# below is a hack. best model related stuff is saved at itr 999999, therefore, simple_save999999.
# Doing this way, I can use test_policy and plot directly to test the best models.
# saved best models includes:
# 1) a copy of the env
# 2) the best rl model with parameters
# 3) a pickle file "best_eval_performance_n_structure" storing best_performance, best_structure and epoch
# note that 1) and 2) are spinningup defaults, and 3) is a custom save
best_eval_AverageEpRet, best_eval_StdEpRet = eval_and_save_best_model(
best_eval_AverageEpRet,
best_eval_StdEpRet,
# a new logger is created and passed in so that the new logger can leverage the directory
# structure without messing up the logger in the training loop
eval_logger=EpochLogger(
**dict(exp_name=logger_kwargs['exp_name'],
output_dir=os.path.join(logger.output_dir,
"simple_save999999"))),
train_logger=logger,
tb_logger=tb_logger,
epoch=epoch,
# the env_name is passed in so that to create an env when and where it is needed. This is to
# logx.save_state() error where an env pointer cannot be pickled
env_name=env_name,
get_action=lambda x: sess.run(
pi, feed_dict={x_ph: x[None, :]})[0])
# Perform VPG update!
update()
# # # Log into tensorboard
log_key_to_tb(tb_logger,
logger,
epoch,
key="EpRet",
with_min_and_max=True)
log_key_to_tb(tb_logger,
logger,
epoch,
key="EpLen",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="VVals",
with_min_and_max=True)
log_key_to_tb(tb_logger,
logger,
epoch,
key="LossPi",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="LossV",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="DeltaLossPi",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="DeltaLossV",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="Entropy",
with_min_and_max=False)
log_key_to_tb(tb_logger,
logger,
epoch,
key="KL",
with_min_and_max=False)
tb_logger.log_scalar(tag="TotalEnvInteracts",
value=(epoch + 1) * steps_per_epoch,
step=epoch)
tb_logger.log_scalar(tag="Time",
value=time.time() - start_time,
step=epoch)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def sac_multistep(
env_fn,
hidden_sizes=[256, 256],
seed=0,
steps_per_epoch=1000,
epochs=1000,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=3e-4,
alpha=0.2,
batch_size=256,
start_steps=10000,
max_ep_len=1000,
save_freq=1,
save_model=False,
auto_alpha=True,
grad_clip=-1,
logger_store_freq=100,
multistep_k=1,
debug=False,
use_single_variant=False,
logger_kwargs=dict(),
):
"""
Largely following OpenAI documentation, but a bit different
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
hidden_sizes: number of entries is number of hidden layers
each entry in this list indicate the size of that hidden layer.
applies to all networks
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch. Note the epoch here is just logging epoch
so every this many steps a logging to stdouot and also output file will happen
note: not to be confused with training epoch which is a term used often in literature for all kinds of
different things
epochs (int): Number of epochs to run and train agent. Usage of this term can be different in different
algorithms, use caution. Here every epoch you get new logs
replay_size (int): Maximum length of replay buffer.
gamma (float): Discount factor. (Always between 0 and 1.)
polyak (float): Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
.. math:: \\theta_{\\text{targ}} \\leftarrow
\\rho \\theta_{\\text{targ}} + (1-\\rho) \\theta
where :math:`\\rho` is polyak. (Always between 0 and 1, usually
close to 1.)
lr (float): Learning rate (used for both policy and value learning).
alpha (float): Entropy regularization coefficient. (Equivalent to
inverse of reward scale in the original SAC paper.)
batch_size (int): Minibatch size for SGD.
start_steps (int): Number of steps for uniform-random action selection,
before running real policy. Helps exploration. However during testing the action always come from policy
max_ep_len (int): Maximum length of trajectory / episode / rollout. Environment will get reseted if
timestep in an episode excedding this number
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
logger_kwargs (dict): Keyword args for EpochLogger.
save_model (bool): set to True if want to save the trained agent
auto_alpha: set to True to use the adaptive alpha scheme, target entropy will be set automatically
grad_clip: whether to use gradient clipping. < 0 means no clipping
logger_store_freq: how many steps to log debugging info, typically don't need to change
"""
if debug:
hidden_sizes = [2, 2]
batch_size = 2
start_steps = 1000
multistep_k = 5
use_single_variant = True
"""set up logger"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
env, test_env = env_fn(), env_fn()
## seed torch and numpy
torch.manual_seed(seed)
np.random.seed(seed)
## seed environment along with env action space so that everything about env is seeded
env.seed(seed)
env.action_space.np_random.seed(seed)
test_env.seed(seed + 10000)
test_env.action_space.np_random.seed(seed + 10000)
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# if environment has a smaller max episode length, then use the environment's max episode length
max_ep_len = env._max_episode_steps if max_ep_len > env._max_episode_steps else max_ep_len
# Action limit for clamping: critically, assumes all dimensions share the same bound!
# we need .item() to convert it from numpy float to python float
act_limit = env.action_space.high[0].item()
# Experience buffer
replay_buffer = MultistepReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
"""
Auto tuning alpha
"""
if auto_alpha:
target_entropy = -np.prod(env.action_space.shape).item() # H
log_alpha = torch.zeros(1, requires_grad=True)
alpha_optim = optim.Adam([log_alpha], lr=lr)
else:
target_entropy, log_alpha, alpha_optim = None, None, None
def test_agent(n=1):
"""
This will test the agent's performance by running n episodes
During the runs, the agent only take deterministic action, so the
actions are not drawn from a distribution, but just use the mean
:param n: number of episodes to run the agent
"""
ep_return_list = np.zeros(n)
for j in range(n):
o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0
while not (d or (ep_len == max_ep_len)):
# Take deterministic actions at test time
a = policy_net.get_env_action(o, deterministic=True)
o, r, d, _ = test_env.step(a)
ep_ret += r
ep_len += 1
ep_return_list[j] = ep_ret
logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
total_steps = steps_per_epoch * epochs
"""init all networks"""
# see line 1
policy_net = TanhGaussianPolicySACAdapt(obs_dim,
act_dim,
hidden_sizes,
action_limit=act_limit)
q1_net = Mlp(obs_dim + act_dim, 1, hidden_sizes)
q2_net = Mlp(obs_dim + act_dim, 1, hidden_sizes)
q1_target_net = Mlp(obs_dim + act_dim, 1, hidden_sizes)
q2_target_net = Mlp(obs_dim + act_dim, 1, hidden_sizes)
# see line 2: copy parameters from value_net to target_value_net
q1_target_net.load_state_dict(q1_net.state_dict())
q2_target_net.load_state_dict(q2_net.state_dict())
# set up optimizers
policy_optimizer = optim.Adam(policy_net.parameters(), lr=lr)
q1_optimizer = optim.Adam(q1_net.parameters(), lr=lr)
q2_optimizer = optim.Adam(q2_net.parameters(), lr=lr)
# mean squared error loss for v and q networks
mse_criterion = nn.MSELoss()
# Main loop: collect experience in env and update/log each epoch
# NOTE: t here is the current number of total timesteps used
# it is not the number of timesteps passed in the current episode
current_update_index = 0
for t in range(total_steps):
"""
Until start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > start_steps:
a = policy_net.get_env_action(o, deterministic=False)
else:
a = env.action_space.sample()
# Step the env, get next observation, reward and done signal
o2, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
d = False if ep_len == max_ep_len else d
# Store experience (observation, action, reward, next observation, done) to replay buffer
# the multi-step buffer (given to you) will store the data in a fashion that
# they can be easily used for multi-step update
replay_buffer.store(o, a, r, o2, d, ep_len, max_ep_len, multistep_k,
gamma)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
"""perform update"""
if replay_buffer.size >= batch_size:
# get data from replay buffer
batch = replay_buffer.sample_batch(batch_size)
obs_tensor = Tensor(batch['obs1'])
# NOTE: given the multi-step buffer, obs_next_tensor now contains the observation that are
# k-step away from current observation
obs_next_tensor = Tensor(batch['obs2'])
acts_tensor = Tensor(batch['acts'])
# NOTE: given the multi-step buffer, rewards tensor now contain the sum of discounted rewards in the next
# k steps (or up until termination, if terminated in less than k steps)
rews_tensor = Tensor(batch['rews']).unsqueeze(1)
# NOTE: given the multi-step buffer, done_tensor now shows whether the data's episode terminated in less
# than k steps or not
done_tensor = Tensor(batch['done']).unsqueeze(1)
"""
now we do a SAC update, following the OpenAI spinup doc
check the openai sac document pseudocode part for reference
line numbers indicate lines in pseudocode part
we will first compute each of the losses
and then update all the networks in the end
"""
# see line 12: get a_tilda, which is newly sampled action (not action from replay buffer)
"""get q loss"""
with torch.no_grad():
a_tilda_next, _, _, log_prob_a_tilda_next, _, _ = policy_net.forward(
obs_next_tensor)
q1_next = q1_target_net(
torch.cat([obs_next_tensor, a_tilda_next], 1))
q2_next = q2_target_net(
torch.cat([obs_next_tensor, a_tilda_next], 1))
# TODO: compute the k-step Q estimate (in the form of reward + next Q), don't worry about the entropy terms
if use_single_variant:
# write code for computing the k-step estimate for the single Q estimate variant case
y_q = rews_tensor + (gamma**multistep_k) * (
1 - done_tensor) * q1_next
else:
# write code for computing the k-step estimate while using double clipped Q
y_q = rews_tensor + (gamma**multistep_k) * (
1 - done_tensor) * torch.min(q1_next, q2_next)
# add the entropy, with a simplied heuristic way
# NOTE: you don't need to modify the following 3 lines. They deal with entropy terms
powers = np.arange(1, multistep_k + 1)
entropy_discounted_sum = -sum(gamma**powers) * (
1 - done_tensor) * alpha * log_prob_a_tilda_next
y_q += entropy_discounted_sum
# JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2]
q1_prediction = q1_net(torch.cat([obs_tensor, acts_tensor], 1))
q1_loss = mse_criterion(q1_prediction, y_q)
q2_prediction = q2_net(torch.cat([obs_tensor, acts_tensor], 1))
q2_loss = mse_criterion(q2_prediction, y_q)
"""
get policy loss
"""
a_tilda, mean_a_tilda, log_std_a_tilda, log_prob_a_tilda, _, _ = policy_net.forward(
obs_tensor)
# see line 12: second equation
q1_a_tilda = q1_net(torch.cat([obs_tensor, a_tilda], 1))
q2_a_tilda = q2_net(torch.cat([obs_tensor, a_tilda], 1))
# TODO write code here to compute policy loss correctly, for both variants.
if use_single_variant:
q_policy_part = q1_a_tilda
else:
q_policy_part = torch.min(q1_a_tilda, q2_a_tilda)
# Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))]
policy_loss = (alpha * log_prob_a_tilda - q_policy_part).mean()
"""
alpha loss, update alpha
"""
if auto_alpha:
alpha_loss = -(
log_alpha *
(log_prob_a_tilda + target_entropy).detach()).mean()
alpha_optim.zero_grad()
alpha_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(log_alpha, grad_clip)
alpha_optim.step()
alpha = log_alpha.exp().item()
else:
alpha_loss = 0
"""update networks"""
q1_optimizer.zero_grad()
q1_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(q1_net.parameters(), grad_clip)
q1_optimizer.step()
q2_optimizer.zero_grad()
q2_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(q2_net.parameters(), grad_clip)
q2_optimizer.step()
policy_optimizer.zero_grad()
policy_loss.backward()
if grad_clip > 0:
nn.utils.clip_grad_norm_(policy_net.parameters(), grad_clip)
policy_optimizer.step()
# see line 16: update target value network with value network
soft_update_model1_with_model2(q1_target_net, q1_net, polyak)
soft_update_model1_with_model2(q2_target_net, q2_net, polyak)
current_update_index += 1
if current_update_index % logger_store_freq == 0:
# store diagnostic info to logger
logger.store(LossPi=policy_loss.item(),
LossQ1=q1_loss.item(),
LossQ2=q2_loss.item(),
LossAlpha=alpha_loss.item(),
Q1Vals=q1_prediction.detach().numpy(),
Q2Vals=q2_prediction.detach().numpy(),
Alpha=alpha,
LogPi=log_prob_a_tilda.detach().numpy())
if d or (ep_len == max_ep_len):
"""when episode terminates, log info about this episode, then reset"""
## store episode return and length to logger
logger.store(EpRet=ep_ret, EpLen=ep_len)
## reset environment
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# End of epoch wrap-up
if (t + 1) % steps_per_epoch == 0:
epoch = t // steps_per_epoch
"""
Save pytorch model, very different from tensorflow version
We need to save the environment, the state_dict of each network
and also the state_dict of each optimizer
"""
if save_model:
sac_state_dict = {
'env': env,
'policy_net': policy_net.state_dict(),
'q1_net': q1_net.state_dict(),
'q2_net': q2_net.state_dict(),
'q1_target_net': q1_target_net.state_dict(),
'q2_target_net': q2_target_net.state_dict(),
'policy_opt': policy_optimizer,
'q1_opt': q1_optimizer,
'q2_opt': q2_optimizer,
'log_alpha': log_alpha,
'alpha_opt': alpha_optim,
'target_entropy': target_entropy
}
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state(sac_state_dict, None)
# use joblib.load(fname) to load
# Test the performance of the deterministic version of the agent.
test_agent()
# TODO write code here to estimate the bias of the Q networks
# recall that we can define the Q bias to be Q value - discounted MC return
# initialize another environment that is only used for provide such a bias estimate
# store that to logger
def estimate_bias(n=1, use_single_variant=False, k=multistep_k):
"""
run n episodes and calculate the mc_return and estimated_q for each appearing states
drop last multistep_k data point of each episode
calculate the q bias using mean(mc_return)-mean(estimated_q)
return q bias and mean(estimated_q)
"""
state_num, mc_ret, est_q = 0, 0, 0
for _ in range(n):
o, r, d, ep_len, ep_mc_ret, reward_list, q_list = bias_test_env.reset(
), 0, False, 0, 0, [], []
while not (d or (ep_len == max_ep_len)):
# Take stochastic actions
a = policy_net.get_env_action(o, deterministic=False)
q1 = q1_net(torch.cat(
[Tensor([o]), Tensor([a])], 1)).item()
q2 = q2_net(torch.cat(
[Tensor([o]), Tensor([a])], 1)).item()
# add estimated q for each state to q_list
# if use_single_variant:
q_list.append(q1)
# else:
# q_list.append(min(q1,q2))
o, r, d, _ = bias_test_env.step(a)
# store each r in reward_list
reward_list.append(r)
ep_len += 1
# drop last 200 terms of the reward list and q list
reward_list = reward_list[:-200]
q_list = q_list[:-200]
# calculate the sum of all mc_returns for each state in the episode
for i in range(len(reward_list)):
powers = np.arange(len(reward_list) - i)
ep_mc_ret += sum(
(gamma**powers) * np.array(reward_list[i:]))
# update mc_ret and est_q
mc_ret = (state_num * mc_ret + ep_mc_ret) / (state_num +
ep_len)
est_q = (state_num * est_q + sum(q_list)) / (state_num +
ep_len)
# calculate bias
return est_q - mc_ret, est_q
bias_test_env = env_fn()
bias_test_env.seed(seed + 10000)
bias_test_env.action_space.np_random.seed(seed + 10000)
bias, est_q = estimate_bias(n=1,
use_single_variant=use_single_variant,
k=multistep_k)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('TestEpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('TotalEnvInteracts', t)
logger.log_tabular('Q1Vals', with_min_and_max=True)
logger.log_tabular('Q2Vals', with_min_and_max=True)
logger.log_tabular('Alpha', with_min_and_max=True)
logger.log_tabular('LossAlpha', average_only=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ1', average_only=True)
logger.log_tabular('LossQ2', average_only=True)
# TODO after you store bias info to logger, you should also write code here to log them
# so that you can later plot them
logger.log_tabular('QBias', bias)
logger.log_tabular('QVals', est_q)
logger.log_tabular('K', multistep_k)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
sys.stdout.flush()
def ppo(env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=1e-3,
train_pi_iters=80,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_critic: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the main
outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``pi`` (batch, act_dim) | Samples actions from policy given
| states.
``logp`` (batch,) | Gives log probability, according to
| the policy, of taking actions ``a_ph``
| in states ``x_ph``.
``logp_pi`` (batch,) | Gives log probability, according to
| the policy, of the action sampled by
| ``pi``.
``v`` (batch,) | Gives the value estimate for states
| in ``x_ph``. (Critical: make sure
| to flatten this!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_critic
function you provided to PPO.
seed (int): Seed for random number generators.
steps_per_epoch (int): Number of steps of interaction (state-action pairs)
for the agent and the environment in each epoch.
epochs (int): Number of epochs of interaction (equivalent to
number of policy updates) to perform.
gamma (float): Discount factor. (Always between 0 and 1.)
clip_ratio (float): Hyperparameter for clipping in the policy objective.
Roughly: how far can the new policy go from the old policy while
still profiting (improving the objective function)? The new policy
can still go farther than the clip_ratio says, but it doesn't help
on the objective anymore. (Usually small, 0.1 to 0.3.)
pi_lr (float): Learning rate for policy optimizer.
vf_lr (float): Learning rate for value function optimizer.
train_pi_iters (int): Maximum number of gradient descent steps to take
on policy loss per epoch. (Early stopping may cause optimizer
to take fewer than this.)
train_v_iters (int): Number of gradient descent steps to take on
value function per epoch.
lam (float): Lambda for GAE-Lambda. (Always between 0 and 1,
close to 1.)
max_ep_len (int): Maximum length of trajectory / episode / rollout.
target_kl (float): Roughly what KL divergence we think is appropriate
between new and old policies after an update. This will get used
for early stopping. (Usually small, 0.01 or 0.05.)
logger_kwargs (dict): Keyword args for EpochLogger.
save_freq (int): How often (in terms of gap between epochs) to save
the current policy and value function.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space,
env.action_space)
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
# Main outputs from computation graph
pi, logp, logp_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# Need all placeholders in *this* order later (to zip with data from buffer)
all_phs = [x_ph, a_ph, adv_ph, ret_ph, logp_old_ph]
# Every step, get: action, value, and logprob
get_action_ops = [pi, v, logp_pi]
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Count variables
var_counts = tuple(core.count_vars(scope) for scope in ['pi', 'v'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# PPO objectives
ratio = tf.exp(logp - logp_old_ph) # pi(a|s) / pi_old(a|s)
min_adv = tf.where(adv_ph > 0, (1 + clip_ratio) * adv_ph,
(1 - clip_ratio) * adv_ph)
pi_loss = -tf.reduce_mean(tf.minimum(ratio * adv_ph, min_adv))
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Info (useful to watch during learning)
approx_kl = tf.reduce_mean(
logp_old_ph -
logp) # a sample estimate for KL-divergence, easy to compute
approx_ent = tf.reduce_mean(
-logp) # a sample estimate for entropy, also easy to compute
clipped = tf.logical_or(ratio > (1 + clip_ratio), ratio < (1 - clip_ratio))
clipfrac = tf.reduce_mean(tf.cast(clipped, tf.float32))
# Optimizers
train_pi = MpiAdamOptimizer(learning_rate=pi_lr).minimize(pi_loss)
train_v = MpiAdamOptimizer(learning_rate=vf_lr).minimize(v_loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'pi': pi, 'v': v})
def update():
inputs = {k: v for k, v in zip(all_phs, buf.get())}
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Training
for i in range(train_pi_iters):
_, kl = sess.run([train_pi, approx_kl], feed_dict=inputs)
kl = mpi_avg(kl)
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
logger.store(StopIter=i)
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
pi_l_new, v_l_new, kl, cf = sess.run(
[pi_loss, v_loss, approx_kl, clipfrac], feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
for t in range(local_steps_per_epoch):
a, v_t, logp_t = sess.run(get_action_ops,
feed_dict={x_ph: o.reshape(1, -1)})
# save and log
buf.store(o, a, r, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(a[0])
ep_ret += r
ep_len += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t == local_steps_per_epoch - 1):
if not (terminal):
print('Warning: trajectory cut off by epoch at %d steps.' %
ep_len)
# if trajectory didn't reach terminal state, bootstrap value target
last_val = r if d else sess.run(
v, feed_dict={x_ph: o.reshape(1, -1)})
buf.finish_path(last_val) #在轨迹的末尾调用finish进行切断
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform PPO update!
update()
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('VVals', with_min_and_max=True)
logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossV', average_only=True)
logger.log_tabular('DeltaLossPi', average_only=True)
logger.log_tabular('DeltaLossV', average_only=True)
logger.log_tabular('Entropy', average_only=True)
logger.log_tabular('KL', average_only=True)
logger.log_tabular('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def train_mnist(steps_per_epoch=100,
epochs=5,
lr=1e-3,
layers=2,
hidden_size=64,
logger_kwargs=dict(),
save_freq=1):
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Load and preprocess MNIST data
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 28 * 28) / 255.0
# Define inputs & main outputs from computation graph
x_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, 28 * 28))
y_ph = tf.compat.v1.placeholder(tf.int32, shape=(None, ))
logits = mlp(x_ph,
hidden_sizes=[hidden_size] * layers + [10],
activation=tf.nn.relu)
predict = tf.argmax(input=logits, axis=1, output_type=tf.int32)
# Define loss function, accuracy, and training op
y = tf.one_hot(y_ph, 10)
loss = tf.compat.v1.losses.softmax_cross_entropy(y, logits)
acc = tf.reduce_mean(
input_tensor=tf.cast(tf.equal(y_ph, predict), tf.float32))
train_op = tf.compat.v1.train.AdamOptimizer().minimize(loss)
# Prepare session
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
# Setup model saving
logger.setup_tf_saver(sess,
inputs={'x': x_ph},
outputs={
'logits': logits,
'predict': predict
})
start_time = time.time()
# Run main training loop
for epoch in range(epochs):
for t in range(steps_per_epoch):
idxs = np.random.randint(0, len(x_train), 32)
feed_dict = {x_ph: x_train[idxs], y_ph: y_train[idxs]}
outs = sess.run([loss, acc, train_op], feed_dict=feed_dict)
logger.store(Loss=outs[0], Acc=outs[1])
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state(state_dict=dict(), itr=None)
# Log info about epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('Acc', with_min_and_max=True)
logger.log_tabular('Loss', average_only=True)
logger.log_tabular('TotalGradientSteps', (epoch + 1) * steps_per_epoch)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()