def run_polopt_agent(env_fn,
agent=PPOAgent(),
actor_critic=mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
render=False,
# Experience collection:
steps_per_epoch=4000,
epochs=50,
max_ep_len=1000,
# Discount factors:
gamma=0.99,
lam=0.97,
cost_gamma=0.99,
cost_lam=0.97,
# Policy learning:
ent_reg=0.,
# Cost constraints / penalties:
cost_lim=25,
penalty_init=1.,
penalty_lr=5e-2,
# KL divergence:
target_kl=0.01,
# Value learning:
vf_lr=1e-3,
vf_iters=80,
# Logging:
logger=None,
logger_kwargs=dict(),
save_freq=1
):
#=========================================================================#
# Prepare logger, seed, and environment in this process #
#=========================================================================#
logger = EpochLogger(**logger_kwargs) if logger is None else logger
logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
env = env_fn()
agent.set_logger(logger)
#=========================================================================#
# Create computation graph for actor and critic (not training routine) #
#=========================================================================#
# Share information about action space with policy architecture
ac_kwargs['action_space'] = env.action_space
# Inputs to computation graph from environment spaces
x_ph, a_ph = placeholders_from_spaces(env.observation_space, env.action_space)
# Inputs to computation graph for batch data
adv_ph, cadv_ph, ret_ph, cret_ph, logp_old_ph = placeholders(*(None for _ in range(5)))
# Inputs to computation graph for special purposes
surr_cost_rescale_ph = tf.compat.v1.placeholder(tf.float32, shape=())
cur_cost_ph = tf.compat.v1.placeholder(tf.float32, shape=())
# Outputs from actor critic
ac_outs = actor_critic(x_ph, a_ph, **ac_kwargs)
pi, logp, logp_pi, pi_info, pi_info_phs, d_kl, ent, v, vc = ac_outs
# Organize placeholders for zipping with data from buffer on updates
buf_phs = [x_ph, a_ph, adv_ph, cadv_ph, ret_ph, cret_ph, logp_old_ph]
buf_phs += values_as_sorted_list(pi_info_phs)
# Organize symbols we have to compute at each step of acting in env
get_action_ops = dict(pi=pi,
v=v,
logp_pi=logp_pi,
pi_info=pi_info)
# If agent is reward penalized, it doesn't use a separate value function
# for costs and we don't need to include it in get_action_ops; otherwise we do.
if not(agent.reward_penalized):
get_action_ops['vc'] = vc
# Count variables
var_counts = tuple(count_vars(scope) for scope in ['pi', 'vf', 'vc'])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d, \t vc: %d\n'%var_counts)
# Make a sample estimate for entropy to use as sanity check
approx_ent = tf.reduce_mean(-logp)
#=========================================================================#
# Create replay buffer #
#=========================================================================#
# Obs/act shapes
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
pi_info_shapes = {k: v.shape.as_list()[1:] for k,v in pi_info_phs.items()}
buf = CPOBuffer(local_steps_per_epoch,
obs_shape,
act_shape,
pi_info_shapes,
gamma,
lam,
cost_gamma,
cost_lam)
#=========================================================================#
# Create computation graph for penalty learning, if applicable #
#=========================================================================#
if agent.use_penalty:
with tf.compat.v1.variable_scope('penalty'):
# param_init = np.log(penalty_init)
param_init = np.log(max(np.exp(penalty_init)-1, 1e-8))
penalty_param = tf.compat.v1.get_variable('penalty_param',
initializer=float(param_init),
trainable=agent.learn_penalty,
dtype=tf.float32)
# penalty = tf.exp(penalty_param)
penalty = tf.nn.softplus(penalty_param)
if agent.learn_penalty:
if agent.penalty_param_loss:
penalty_loss = -penalty_param * (cur_cost_ph - cost_lim)
else:
penalty_loss = -penalty * (cur_cost_ph - cost_lim)
train_penalty = MpiAdamOptimizer(learning_rate=penalty_lr).minimize(penalty_loss)
#=========================================================================#
# Create computation graph for policy learning #
#=========================================================================#
# Likelihood ratio
ratio = tf.exp(logp - logp_old_ph)
# Surrogate advantage / clipped surrogate advantage
if agent.clipped_adv:
min_adv = tf.where(adv_ph>0,
(1+agent.clip_ratio)*adv_ph,
(1-agent.clip_ratio)*adv_ph
)
surr_adv = tf.reduce_mean(tf.minimum(ratio * adv_ph, min_adv))
else:
surr_adv = tf.reduce_mean(ratio * adv_ph)
# Surrogate cost
surr_cost = tf.reduce_mean(ratio * cadv_ph)
# Create policy objective function, including entropy regularization
pi_objective = surr_adv + ent_reg * ent
# Possibly include surr_cost in pi_objective
if agent.objective_penalized:
pi_objective -= penalty * surr_cost
pi_objective /= (1 + penalty)
# Loss function for pi is negative of pi_objective
pi_loss = -pi_objective
# Optimizer-specific symbols
if agent.trust_region:
# Symbols needed for CG solver for any trust region method
pi_params = get_vars('pi')
flat_g = tro.flat_grad(pi_loss, pi_params)
v_ph, hvp = tro.hessian_vector_product(d_kl, pi_params)
if agent.damping_coeff > 0:
hvp += agent.damping_coeff * v_ph
# Symbols needed for CG solver for CPO only
flat_b = tro.flat_grad(surr_cost, pi_params)
# Symbols for getting and setting params
get_pi_params = tro.flat_concat(pi_params)
set_pi_params = tro.assign_params_from_flat(v_ph, pi_params)
training_package = dict(flat_g=flat_g,
flat_b=flat_b,
v_ph=v_ph,
hvp=hvp,
get_pi_params=get_pi_params,
set_pi_params=set_pi_params)
elif agent.first_order:
# Optimizer for first-order policy optimization
train_pi = MpiAdamOptimizer(learning_rate=agent.pi_lr).minimize(pi_loss)
# Prepare training package for agent
training_package = dict(train_pi=train_pi)
else:
raise NotImplementedError
# Provide training package to agent
training_package.update(dict(pi_loss=pi_loss,
surr_cost=surr_cost,
d_kl=d_kl,
target_kl=target_kl,
cost_lim=cost_lim))
agent.prepare_update(training_package)
#=========================================================================#
# Create computation graph for value learning #
#=========================================================================#
# Value losses
v_loss = tf.reduce_mean((ret_ph - v)**2)
vc_loss = tf.reduce_mean((cret_ph - vc)**2)
# If agent uses penalty directly in reward function, don't train a separate
# value function for predicting cost returns. (Only use one vf for r - p*c.)
if agent.reward_penalized:
total_value_loss = v_loss
else:
total_value_loss = v_loss + vc_loss
# Optimizer for value learning
train_vf = MpiAdamOptimizer(learning_rate=vf_lr).minimize(total_value_loss)
#=========================================================================#
# Create session, sync across procs, and set up saver #
#=========================================================================#
sess = tf.compat.v1.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, 'vc': vc})
#=========================================================================#
# Provide session to agent #
#=========================================================================#
agent.prepare_session(sess)
#=========================================================================#
# Create function for running update (called at end of each epoch) #
#=========================================================================#
def update():
cur_cost = logger.get_stats('EpCost')[0]
c = cur_cost - cost_lim
if c > 0 and agent.cares_about_cost:
logger.log('Warning! Safety constraint is already violated.', 'red')
#=====================================================================#
# Prepare feed dict #
#=====================================================================#
inputs = {k:v for k,v in zip(buf_phs, buf.get())}
inputs[surr_cost_rescale_ph] = logger.get_stats('EpLen')[0]
inputs[cur_cost_ph] = cur_cost
#=====================================================================#
# Make some measurements before updating #
#=====================================================================#
measures = dict(LossPi=pi_loss,
SurrCost=surr_cost,
LossV=v_loss,
Entropy=ent)
if not(agent.reward_penalized):
measures['LossVC'] = vc_loss
if agent.use_penalty:
measures['Penalty'] = penalty
pre_update_measures = sess.run(measures, feed_dict=inputs)
logger.store(**pre_update_measures)
#=====================================================================#
# Update penalty if learning penalty #
#=====================================================================#
if agent.learn_penalty:
sess.run(train_penalty, feed_dict={cur_cost_ph: cur_cost})
#=====================================================================#
# Update policy #
#=====================================================================#
agent.update_pi(inputs)
#=====================================================================#
# Update value function #
#=====================================================================#
for _ in range(vf_iters):
sess.run(train_vf, feed_dict=inputs)
#=====================================================================#
# Make some measurements after updating #
#=====================================================================#
del measures['Entropy']
measures['KL'] = d_kl
post_update_measures = sess.run(measures, feed_dict=inputs)
deltas = dict()
for k in post_update_measures:
if k in pre_update_measures:
deltas['Delta'+k] = post_update_measures[k] - pre_update_measures[k]
logger.store(KL=post_update_measures['KL'], **deltas)
#=========================================================================#
# Run main environment interaction loop #
#=========================================================================#
start_time = time.time()
o, r, d, c, ep_ret, ep_cost, ep_len = env.reset(), 0, False, 0, 0, 0, 0
cur_penalty = 0
cum_cost = 0
for epoch in range(epochs):
if agent.use_penalty:
cur_penalty = sess.run(penalty)
# will gather 30,000 state, action, next state
for t in range(local_steps_per_epoch):
# Possibly render
if render and proc_id()==0 and t < 1000:
env.render()
# Get outputs from policy
get_action_outs = sess.run(get_action_ops,
feed_dict={x_ph: o[np.newaxis]})
a = get_action_outs['pi']
v_t = get_action_outs['v']
vc_t = get_action_outs.get('vc', 0) # Agent may not use cost value func
logp_t = get_action_outs['logp_pi']
pi_info_t = get_action_outs['pi_info']
# Step in environment
o2, r, d, info = env.step(a)
# Include penalty on cost
c = info.get('cost', 0)
# Track cumulative cost over training
cum_cost += c
# save and log
if agent.reward_penalized:
r_total = r - cur_penalty * c
r_total = r_total / (1 + cur_penalty)
buf.store(o, a, r_total, v_t, 0, 0, logp_t, pi_info_t)
else:
buf.store(o, a, r, v_t, c, vc_t, logp_t, pi_info_t)
logger.store(VVals=v_t, CostVVals=vc_t)
o = o2
ep_ret += r
ep_cost += c
ep_len += 1
"""
t=0 t = 30,000
| 30,000 local_steps_per_epoch |
| ep | ep | ep | ep | ep | ep |
max 1000
"""
# reach the goal or hit max env timesteps (1000)
terminal = (d or (ep_len == max_ep_len))
if terminal or (t==local_steps_per_epoch-1):
# If trajectory didn't reach terminal state, bootstrap value target(s)
if d and not(ep_len == max_ep_len):
# Note: we do not count env time out as true terminal state
last_val, last_cval = 0, 0
else:
feed_dict={x_ph: o[np.newaxis]}
if agent.reward_penalized:
last_val = sess.run(v, feed_dict=feed_dict)
last_cval = 0
else:
last_val, last_cval = sess.run([v, vc], feed_dict=feed_dict)
buf.finish_path(last_val, last_cval)
# Only save EpRet / EpLen if trajectory finished
if terminal:
logger.store(EpRet=ep_ret, EpLen=ep_len, EpCost=ep_cost)
else:
print('Warning: trajectory cut off by epoch at %d steps.'%ep_len)
# Reset environment
o, r, d, c, ep_ret, ep_len, ep_cost = env.reset(), 0, False, 0, 0, 0, 0
def sac(env_fn,
actor_fn=mlp_actor,
critic_fn=mlp_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=1000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=1e-4,
batch_size=1024,
local_start_steps=int(1e3),
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=10,
local_update_after=int(1e3),
update_freq=1,
render=False,
fixed_entropy_bonus=None,
entropy_constraint=-1.0,
fixed_cost_penalty=None,
cost_constraint=None,
cost_lim=None,
reward_scale=1,
penalty_lr=5e-2,
use_discor=False,
cost_maxq=True):
"""
Args:
env_fn : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
actor_fn: A function which takes in placeholder symbols
for state, ``x_ph``, and action, ``a_ph``, and returns the actor
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.
=========== ================ ======================================
critic_fn: A function which takes in placeholder symbols
for state, ``x_ph``, action, ``a_ph``, and policy ``pi``,
and returns the critic outputs from the agent's Tensorflow computation graph:
=========== ================ ======================================
Symbol Shape Description
=========== ================ ======================================
``critic`` (batch,) | Gives one estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
``critic_pi`` (batch,) | Gives another estimate of Q* for
| states in ``x_ph`` and actions in
| ``a_ph``.
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the actor_fn / critic_fn
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).
batch_size (int): Minibatch size for SGD.
local_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.
fixed_entropy_bonus (float or None): Fixed bonus to reward for entropy.
Units are (points of discounted sum of future reward) / (nats of policy entropy).
If None, use ``entropy_constraint`` to set bonus value instead.
entropy_constraint (float): If ``fixed_entropy_bonus`` is None,
Adjust entropy bonus to maintain at least this much entropy.
Actual constraint value is multiplied by the dimensions of the action space.
Units are (nats of policy entropy) / (action dimenson).
fixed_cost_penalty (float or None): Fixed penalty to reward for cost.
Units are (points of discounted sum of future reward) / (points of discounted sum of future costs).
If None, use ``cost_constraint`` to set penalty value instead.
cost_constraint (float or None): If ``fixed_cost_penalty`` is None,
Adjust cost penalty to maintain at most this much cost.
Units are (points of discounted sum of future costs).
Note: to get an approximate cost_constraint from a cost_lim (undiscounted sum of costs),
multiply cost_lim by (1 - gamma ** episode_len) / (1 - gamma).
If None, use cost_lim to calculate constraint.
cost_lim (float or None): If ``cost_constraint`` is None,
calculate an approximate constraint cost from this cost limit.
Units are (expectation of undiscounted sum of costs in a single episode).
If None, cost_lim is not used, and if no cost constraints are used, do naive optimization.
"""
use_costs = fixed_cost_penalty or cost_constraint or cost_lim
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# Env instantiation
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
# Setting seeds
tf.set_random_seed(seed)
np.random.seed(seed)
env.seed(seed)
test_env.seed(seed)
# 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, c_ph = placeholders(obs_dim, act_dim,
obs_dim, None, None,
None)
# Main outputs from computation graph
with tf.variable_scope('main'):
mu, pi, logp_pi = actor_fn(x_ph, a_ph, **ac_kwargs)
qr1, qr1_pi = critic_fn(x_ph, a_ph, pi, name='qr1', **ac_kwargs)
qr2, qr2_pi = critic_fn(x_ph, a_ph, pi, name='qr2', **ac_kwargs)
qc1, qc1_pi = critic_fn(x_ph, a_ph, pi, name='qc1', **ac_kwargs)
if cost_maxq:
qc2, qc2_pi = critic_fn(x_ph, a_ph, pi, name='qc2', **ac_kwargs)
if use_discor:
er1, er1_targ = critic_fn(x_ph, a_ph, pi, name='er1', **ac_kwargs)
er2, er2_targ = critic_fn(x_ph, a_ph, pi, name='er2', **ac_kwargs)
ec1, ec1_targ = critic_fn(x_ph, a_ph, pi, name='ec1', **ac_kwargs)
if cost_maxq:
ec2, ec2_targ = critic_fn(x_ph,
a_ph,
pi,
name='ec2',
**ac_kwargs)
with tf.variable_scope('main', reuse=True):
# Additional policy output from a different observation placeholder
# This lets us do separate optimization updates (actor, critics, etc)
# in a single tensorflow op.
_, pi2, logp_pi2 = actor_fn(x2_ph, a_ph, **ac_kwargs)
# Target value network
with tf.variable_scope('target'):
_, qr1_pi_targ = critic_fn(x2_ph, a_ph, pi2, name='qr1', **ac_kwargs)
_, qr2_pi_targ = critic_fn(x2_ph, a_ph, pi2, name='qr2', **ac_kwargs)
_, qc1_pi_targ = critic_fn(x2_ph, a_ph, pi2, name='qc1', **ac_kwargs)
if cost_maxq:
_, qc2_pi_targ = critic_fn(x2_ph,
a_ph,
pi2,
name='qc2',
**ac_kwargs)
if use_discor:
_, er1_pi_targ = critic_fn(x_ph, a_ph, pi, name='er1', **ac_kwargs)
_, er2_pi_targ = critic_fn(x_ph, a_ph, pi, name='er2', **ac_kwargs)
_, ec1_pi_targ = critic_fn(x_ph, a_ph, pi, name='ec1', **ac_kwargs)
if cost_maxq:
_, ec2_pi_targ = critic_fn(x_ph,
a_ph,
pi,
name='ec2',
**ac_kwargs)
# Entropy bonus
if fixed_entropy_bonus is None:
with tf.variable_scope('entreg'):
soft_alpha = tf.get_variable('soft_alpha',
initializer=0.0,
trainable=True,
dtype=tf.float32)
alpha = tf.nn.softplus(soft_alpha)
else:
alpha = tf.constant(fixed_entropy_bonus)
log_alpha = tf.log(alpha)
# Cost penalty
if use_costs:
if fixed_cost_penalty is None:
with tf.variable_scope('costpen'):
soft_beta = tf.get_variable('soft_beta',
initializer=0.0,
trainable=True,
dtype=tf.float32)
beta = tf.nn.softplus(soft_beta)
log_beta = tf.log(beta)
else:
beta = tf.constant(fixed_cost_penalty)
log_beta = tf.log(beta)
else:
beta = 0.0 # costs do not contribute to policy optimization
print('Not using costs')
if use_discor:
with tf.variable_scope('discor'):
tr1 = tf.get_variable('tr1',
initializer=10.0,
trainable=False,
dtype=tf.float32)
tr2 = tf.get_variable('tr2',
initializer=10.0,
trainable=False,
dtype=tf.float32)
tc1 = tf.get_variable('tc1',
initializer=10.0,
trainable=False,
dtype=tf.float32)
if cost_maxq:
tc2 = tf.get_variable('tc2',
initializer=10.0,
trainable=False,
dtype=tf.float32)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Count variables
if proc_id() == 0:
var_counts = tuple(
count_vars(scope) for scope in
['main/pi', 'main/qr1', 'main/qr2', 'main/qc1', 'main'])
print((
'\nNumber of parameters: \t pi: %d, \t qr1: %d, \t qr2: %d, \t qc1: %d, \t total: %d\n'
) % var_counts)
# Min Double-Q:
min_q_pi = tf.minimum(qr1_pi, qr2_pi)
min_q_pi_targ = tf.minimum(qr1_pi_targ, qr2_pi_targ)
if cost_maxq:
max_qc_pi = tf.maximum(qc1_pi, qc2_pi)
max_qc_pi_targ = tf.maximum(qc1_pi_targ, qc2_pi_targ)
else:
max_qc_pi = qc1_pi
max_qc_pi_targ = qc1_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_pi2))
qc_backup = tf.stop_gradient(c_ph + gamma * (1 - d_ph) * max_qc_pi_targ)
if use_discor:
er1_backup = tf.stop_gradient(
tf.abs(qr1 - q_backup) + gamma * (1 - d_ph) * er1_pi_targ)
er2_backup = tf.stop_gradient(
tf.abs(qr2 - q_backup) + gamma * (1 - d_ph) * er2_pi_targ)
ec1_backup = tf.stop_gradient(
tf.abs(qc1 - qc_backup) + gamma * (1 - d_ph) * ec1_pi_targ)
if cost_maxq:
ec2_backup = tf.stop_gradient(
tf.abs(qc2 - qc_backup) + gamma * (1 - d_ph) * ec2_pi_targ)
qr1_loss = 0.5 * tf.reduce_sum(
tf.nn.softmax(er1_backup / tr1, axis=0) * (q_backup - qr1)**2)
qr2_loss = 0.5 * tf.reduce_sum(
tf.nn.softmax(er2_backup / tr2, axis=0) * (q_backup - qr2)**2)
qc1_loss = 0.5 * tf.reduce_sum(
tf.nn.softmax(ec1_backup / tc1, axis=0) * (qc_backup - qc1)**2)
if cost_maxq:
qc2_loss = 0.5 * tf.reduce_sum(
tf.nn.softmax(ec2_backup / tc2, axis=0) * (qc_backup - qc2)**2)
else:
qr1_loss = 0.5 * tf.reduce_mean((q_backup - qr1)**2)
qr2_loss = 0.5 * tf.reduce_mean((q_backup - qr2)**2)
qc1_loss = 0.5 * tf.reduce_mean((qc_backup - qc1)**2)
if cost_maxq:
qc2_loss = 0.5 * tf.reduce_mean((qc_backup - qc2)**2)
# Soft actor-critic losses
q_loss = qr1_loss + qr2_loss + qc1_loss
if cost_maxq:
q_loss += qc2_loss
pi_loss = tf.reduce_mean(alpha * logp_pi - min_q_pi +
beta * max_qc_pi) / (1 + beta)
if use_discor:
er1_loss = 0.5 * tf.reduce_mean((er1_backup - er1)**2)
er2_loss = 0.5 * tf.reduce_mean((er2_backup - er2)**2)
ec1_loss = 0.5 * tf.reduce_mean((ec1_backup - ec1)**2)
error_loss = er1_loss + er2_loss + ec1_loss
if cost_maxq:
ec2_loss = 0.5 * tf.reduce_mean((ec2_backup - ec2)**2)
error_loss += +ec2_loss
ec2_mean = tf.reduce_mean(ec2)
er1_mean = tf.reduce_mean(er1)
er2_mean = tf.reduce_mean(er2)
ec1_mean = tf.reduce_mean(ec1)
# Loss for alpha
entropy_constraint *= act_dim
pi_entropy = -tf.reduce_mean(logp_pi)
# alpha_loss = - soft_alpha * (entropy_constraint - pi_entropy)
alpha_loss = -alpha * (entropy_constraint - pi_entropy)
print('using entropy constraint', entropy_constraint)
# Loss for beta
if use_costs and not fixed_cost_penalty:
if cost_constraint is None:
# Convert assuming equal cost accumulated each step
# Note this isn't the case, since the early in episode doesn't usually have cost,
# but since our algorithm optimizes the discounted infinite horizon from each entry
# in the replay buffer, we should be approximately correct here.
# It's worth checking empirical total undiscounted costs to see if they match.
cost_constraint = cost_lim * (1 - gamma**max_ep_len) / (
1 - gamma) / max_ep_len
print('using cost constraint', cost_constraint)
beta_loss = beta * (cost_constraint - qc1)
#TODO: What is the correct target here?
# Policy train op
# (has to be separate from value train op, because qr1_pi appears in pi_loss)
train_pi_op = MpiAdamOptimizer(learning_rate=lr).minimize(
pi_loss, var_list=get_vars('main/pi'), name='train_pi')
# Value train op
with tf.control_dependencies([train_pi_op]):
train_q_op = MpiAdamOptimizer(learning_rate=lr).minimize(
q_loss, var_list=get_vars('main/q'), name='train_q')
with tf.control_dependencies([train_q_op]):
if use_discor:
train_e_op = MpiAdamOptimizer(learning_rate=lr).minimize(
error_loss, var_list=get_vars('main/e'), name='train_e')
with tf.control_dependencies([train_e_op]):
if cost_maxq:
train_e_out_op = tf.group([
tf.assign(tr1, (1 - polyak) * er1_mean + polyak * tr1),
tf.assign(tr2, (1 - polyak) * er2_mean + polyak * tr2),
tf.assign(tc1, (1 - polyak) * ec1_mean + polyak * tc1),
tf.assign(tc2, (1 - polyak) * ec2_mean + polyak * tc2)
])
else:
train_e_out_op = tf.group([
tf.assign(tr1, (1 - polyak) * er1_mean + polyak * tr1),
tf.assign(tr2, (1 - polyak) * er2_mean + polyak * tr2),
tf.assign(tc1, (1 - polyak) * ec1_mean + polyak * tc1)
])
else:
train_e_out_op = tf.no_op()
if fixed_entropy_bonus is None:
entreg_optimizer = MpiAdamOptimizer(learning_rate=lr)
with tf.control_dependencies([train_e_out_op]):
train_entreg_op = entreg_optimizer.minimize(
alpha_loss, var_list=get_vars('entreg'))
if use_costs and fixed_cost_penalty is None:
costpen_optimizer = MpiAdamOptimizer(learning_rate=penalty_lr)
with tf.control_dependencies([train_entreg_op]):
train_costpen_op = costpen_optimizer.minimize(
beta_loss, var_list=get_vars('costpen'))
# Polyak averaging for target variables
target_update = get_target_update('main', 'target', polyak)
# Single monolithic update with explicit control dependencies
with tf.control_dependencies([train_pi_op]):
with tf.control_dependencies([train_q_op]):
if use_discor:
with tf.control_dependencies([train_e_op]):
with tf.control_dependencies([train_e_out_op]):
grouped_update = tf.group([target_update])
else:
grouped_update = tf.group([target_update])
if fixed_entropy_bonus is None:
grouped_update = tf.group([grouped_update, train_entreg_op])
if use_costs and fixed_cost_penalty is None:
grouped_update_a = tf.group([grouped_update, train_costpen_op])
# Initializing targets to match main variables
# As a shortcut, use our exponential moving average update w/ coefficient zero
target_init = get_target_update('main', 'target', 0.0)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(target_init)
# Sync params across processes
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess,
inputs={
'x': x_ph,
'a': a_ph
},
outputs={
'mu': mu,
'pi': pi,
'qr1': qr1,
'qr2': qr2,
'qc1': qc1
})
def get_action(o, deterministic=False):
act_op = mu if deterministic else pi
return sess.run(act_op, feed_dict={x_ph: o.reshape(1, -1)})[0]
def test_agent(n=10):
for j in range(n):
o, r, d, ep_ret, ep_cost, ep_len, ep_goals, = test_env.reset(
), 0, False, 0, 0, 0, 0
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))
if render and proc_id() == 0 and j == 0:
test_env.render()
ep_ret += r
ep_cost += info.get('cost', 0)
ep_len += 1
ep_goals += 1 if info.get('goal_met', False) else 0
logger.store(TestEpRet=ep_ret,
TestEpCost=ep_cost,
TestEpLen=ep_len,
TestEpGoals=ep_goals)
start_time = time.time()
o, r, d, ep_ret, ep_cost, ep_len, ep_goals = env.reset(
), 0, False, 0, 0, 0, 0
total_steps = steps_per_epoch * epochs
# variables to measure in an update
vars_to_get = dict(LossPi=pi_loss,
LossQR1=qr1_loss,
LossQR2=qr2_loss,
LossQC1=qc1_loss,
QR1Vals=qr1,
QR2Vals=qr2,
QC1Vals=qc1,
LogPi=logp_pi,
PiEntropy=pi_entropy,
Alpha=alpha,
LogAlpha=log_alpha,
LossAlpha=alpha_loss)
if use_costs and not fixed_cost_penalty:
vars_to_get.update(
dict(Beta=beta, LogBeta=log_beta, LossBeta=beta_loss))
if use_discor:
vars_to_get.update(dict(TR1=tr1))
print('starting training', proc_id())
# Main loop: collect experience in env and update/log each epoch
local_steps = 0
local_steps_per_epoch = steps_per_epoch // num_procs()
local_batch_size = batch_size // num_procs()
epoch_start_time = time.time()
for t in range(total_steps // num_procs()):
"""
Until local_start_steps have elapsed, randomly sample actions
from a uniform distribution for better exploration. Afterwards,
use the learned policy.
"""
if t > local_start_steps:
a = get_action(o)
else:
a = env.action_space.sample()
# Step the env
o2, r, d, info = env.step(a)
r *= reward_scale # yee-haw
c = info.get('cost', 0)
ep_ret += r
ep_cost += c
ep_len += 1
ep_goals += 1 if info.get('goal_met', False) else 0
local_steps += 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, c)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret,
EpCost=ep_cost,
EpLen=ep_len,
EpGoals=ep_goals)
o, r, d, ep_ret, ep_cost, ep_len, ep_goals = env.reset(
), 0, False, 0, 0, 0, 0
if t > 0 and t % update_freq == 0:
for j in range(update_freq):
batch = replay_buffer.sample_batch(local_batch_size)
feed_dict = {
x_ph: batch['obs1'],
x2_ph: batch['obs2'],
a_ph: batch['acts'],
r_ph: batch['rews'],
c_ph: batch['costs'],
d_ph: batch['done'],
}
if t < local_update_after:
logger.store(**sess.run(vars_to_get, feed_dict))
else:
if (not j == update_freq -
1) or not (use_costs and not fixed_cost_penalty):
values, _ = sess.run([vars_to_get, grouped_update],
feed_dict)
logger.store(**values)
else:
values, _ = sess.run([vars_to_get, grouped_update_a],
feed_dict)
logger.store(**values)
# End of epoch wrap-up
if t > 0 and t % local_steps_per_epoch == 0:
epoch = t // local_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_start_time = time.time()
test_agent()
logger.store(TestTime=time.time() - test_start_time)
logger.store(EpochTime=time.time() - epoch_start_time)
epoch_start_time = time.time()
# 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('EpCost', with_min_and_max=True)
logger.log_tabular('TestEpCost', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('TestEpLen', average_only=True)
logger.log_tabular('EpGoals', average_only=True)
logger.log_tabular('TestEpGoals', average_only=True)
logger.log_tabular('TotalEnvInteracts', mpi_sum(local_steps))
logger.log_tabular('QR1Vals', with_min_and_max=True)
logger.log_tabular('QR2Vals', with_min_and_max=True)
logger.log_tabular('QC1Vals', with_min_and_max=True)
logger.log_tabular('LogPi', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQR1', average_only=True)
logger.log_tabular('LossQR2', average_only=True)
logger.log_tabular('LossQC1', average_only=True)
logger.log_tabular('LossAlpha', average_only=True)
logger.log_tabular('LogAlpha', average_only=True)
logger.log_tabular('Alpha', average_only=True)
if use_costs and not fixed_cost_penalty:
logger.log_tabular('LossBeta', average_only=True)
logger.log_tabular('LogBeta', average_only=True)
logger.log_tabular('Beta', average_only=True)
if use_discor:
logger.log_tabular('TR1', average_only=True)
logger.log_tabular('PiEntropy', average_only=True)
logger.log_tabular('TestTime', average_only=True)
logger.log_tabular('EpochTime', average_only=True)
logger.log_tabular('TotalTime', time.time() - start_time)
logger.dump_tabular()