def ppo(env_fn,
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=50,
gamma=0.99,
beta=0.01,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=3e-4,
train_pi_iters=80,
train_v_iters=80,
lam=0.95,
max_ep_len=1000,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=10,
use_rnn=False,
reward_factor=1,
spectrum_repr=False):
"""
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()
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
print(ac)
# udpate env config
# env.scalar_thick = ac_kwargs['scalar_thick']
env.update_with_ac(**ac_kwargs)
# For Tuple spaces
obs_dim = ac.obs_dim
if isinstance(env.action_space, spaces.Tuple):
act_dim = core.tuple_space_dim(env.action_space, action=True)
else:
act_dim = env.action_space.shape
# Create actor-critic module
# print(ac)
# 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,
cell_size=ac_kwargs['cell_size'])
# Set up function for computing PPO policy loss
def compute_loss_pi(data):
obs, act, adv, logp_old, hid = data['obs'], data['act'], data[
'adv'], data['logp'], data['hid']
# for i in range(len(obs)-1):
# if torch.eq(obs[i], torch.zeros(12)).sum()==12 and torch.eq(obs[i+1], torch.zeros(12)).sum()==12:
# print(obs[i], obs[i+1], act[i], act[i+1])
# Policy loss
pis = []
logp = 0
if len(ac.pi) > 1: # tuple actions
for i, actor_i in enumerate(ac.pi):
pi, logp_i = actor_i(obs, act[:, i][:, None])
logp += logp_i
pis.append(pi)
else:
pi, logp_i = ac.pi[0](obs, act)
logp += logp_i
pis.append(pi)
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
# sample estimation policy KL
approx_kl = (logp_old - logp).mean().item()
ent = sum([pi.entropy().mean().item() for pi in pis])
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 0.5 * ((ac.v(obs) - ret)**2).mean()
def compute_loss_pi_v_rnn(data):
obs, act, adv, logp_old, ret = data['obs'], data['act'], data[
'adv'], data['logp'], data['ret']
hid = torch.zeros(ac_kwargs['cell_size'])
v = []
logp = []
ent = []
num_traj = 0
#todo: test
for i in range(len(obs)):
v_i, logp_i, hid, ent_i = ac.evaluate(obs[i], act[i], hid)
if i < len(obs) - 1 and obs[i + 1].sum() == 0:
num_traj += 1
# print('Reinitialize #{}'.format(num_traj), flush=True)
hid = torch.zeros(ac_kwargs['cell_size'])
v.append(v_i)
logp.append(logp_i)
ent.append(ent_i)
logp = torch.cat(logp)
v = torch.cat(v)
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()
# print(logp_old - logp)
approx_kl = (logp_old - logp).mean().item()
ent = torch.stack(ent).mean()
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)
loss_v = 0.5 * ((v - ret)**2).mean()
# import pdb; pdb.set_trace()
loss_pi = loss_pi - beta * ent
logger.store(RetBuf=ret.clone().detach().numpy())
# import pdb; pdb.set_trace()
return loss_pi, pi_info, loss_v
# 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)
if use_rnn:
optimizer = Adam(ac.parameters(), lr=pi_lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update():
data = buf.get()
# import pdb; pdb.set_trace()
if not use_rnn:
pi_l_old, pi_info_old = compute_loss_pi(data)
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()
if not use_rnn:
loss_v = compute_loss_v(data)
loss_v.backward()
mpi_avg_grads(ac.v) # average grads across MPI processes
vf_optimizer.step()
else:
pi_l_old, pi_info_old, v_l_old = compute_loss_pi_v_rnn(data)
pi_l_old = pi_l_old.item()
for i in range(train_pi_iters):
optimizer.zero_grad()
loss_pi, pi_info, loss_v = compute_loss_pi_v_rnn(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 = loss_pi + loss_v
loss.backward()
mpi_avg_grads(ac)
optimizer.step()
logger.store(StopIter=i)
# import pdb; pdb.set_trace()
# 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))
# Prepare for interaction with environment
start_time = time.time()
obs, ep_ret, ep_len = env.reset(), 0, 0
# import pdb; pdb.set_trace()
# if ac_kwargs['scalar_thick']:
# thick= obs[env.num_materials:env.num_materials+env.num_thicknesses].argmax() / env.num_thicknesses
# obs = np.concatenate((obs[:env.num_materials+1], np.array([thick])))
# if ac_kwargs['scalar_thick']:
# thick= obs[env.num_materials:env.num_materials+env.num_thicknesses].argmax() / env.num_thicknesses
# obs = np.concatenate((obs[:env.num_materials+1], np.array([thick])))
hid = np.zeros(
ac_kwargs['cell_size']) if ac_kwargs['cell_size'] else np.zeros(1)
# import pdb; pdb.set_trace()
design_tracker = DesignTracker(epochs, **logger_kwargs)
total_env_time = 0
# Main loop: collect experience in env and update/log each epoch
for epoch in range(epochs):
epoch_start_time = time.time()
for t in range(local_steps_per_epoch):
#TODO: only evaluate
act, v, logp, hid = ac.step(
torch.as_tensor(obs, dtype=torch.float32),
torch.as_tensor(hid, dtype=torch.float32))
# nv_start = time.time()
next_obs, r, d, _ = env.step(act)
# env_end = time.time()
# env_time = env_end - env_start
# total_env_time += env_time
r = r * reward_factor # scale the rewards, possibly match the reward scale of atari
ep_ret += r
if not d:
ep_len += 1
# save and log
if use_rnn:
buf.store(obs, act, r, v, logp, hid)
else:
buf.store(obs, act, r, v, logp)
logger.store(VVals=v)
# Update obs (critical!)
obs = next_obs
timeout = ep_len == max_ep_len
terminal = d or timeout
epoch_ended = t == local_steps_per_epoch - 1
if terminal or epoch_ended:
# print(t)
# 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:
if not terminal:
_, v, _, _ = ac.step(
torch.as_tensor(obs, dtype=torch.float32),
torch.as_tensor(hid, 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)
if hasattr(env, 'layers') and hasattr(env, 'thicknesses'):
design_tracker.store(env.layers, env.thicknesses,
ep_ret, epoch)
if rank == 0:
print(env.layers, env.thicknesses)
obs, ep_ret, ep_len = env.reset(), 0, 0
# reinitilize hidden state
hid = np.zeros(ac_kwargs['cell_size'])
if hasattr(env, "layers"):
logger.store(Act=act[1])
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
design_tracker.save_state()
# Perform PPO update!
update()
elapsed = time.time() - start_time
epoch_time = time.time() - epoch_start_time
# 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)
if hasattr(env, 'layers'):
logger.log_tabular('Act', with_min_and_max=True)
logger.log_tabular('RetBuf', 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('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', elapsed)
logger.log_tabular('FPS', int(steps_per_epoch / epoch_time))
logger.dump_tabular()
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):
"""
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.
"""
# 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
# obs_dim = env.observation_space.n
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 = 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)
bayes_kl_loss = 0.
if isinstance(ac.v, BayesMLPCritic):
bayes_kl_loss = ac.v.compute_kl()
total_loss_v = loss_v + bayes_kl_loss / data['obs'].shape[0]
total_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),
BayesKL=bayes_kl_loss)
# Prepare for interaction with environment
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
epoch_reward = []
# 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
epoch_reward.append(ep_ret)
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()
if epoch % 10 == 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('BayesKL', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.dump_tabular()
return epoch_reward
def sppo(args,
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,
train_pi_iters=80,
train_v_iters=80,
lam=0.97,
max_ep_len=200,
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)
###########
if args.alpha == 'auto':
target_entropy = 0.35
log_alpha = tf.get_variable('log_alpha',
dtype=tf.float32,
initializer=tf.log(0.2))
alpha = tf.exp(log_alpha)
else:
alpha = args.alpha
###########
# Main outputs from computation graph
mu, pi, logp, logp_pi, v, q, h = actor_critic(alpha, 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, h]
# 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)
######
if args.alpha == 'auto':
alpha_loss = tf.reduce_mean(
-log_alpha * tf.stop_gradient(-h + target_entropy)
) # tf.clip_by_value(-h + target_entropy, 0.0, 1000.0 )
alpha_optimizer = MpiAdamOptimizer(learning_rate=1e-5)
train_alpha_op = alpha_optimizer.minimize(loss=alpha_loss,
var_list=[log_alpha])
######
# PPO objectives
ratio = tf.exp(logp - logp_old_ph) # pi(a|s) / pi_old(a|s)
# For PPO
# 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))
# ### Scheme1: SPPO NO.2: add entropy
# adv_logp = adv_ph - tf.stop_gradient(alpha) * tf.stop_gradient(logp)
# min_adv = tf.where(adv_logp>0, (1+clip_ratio)*adv_logp, (1-clip_ratio)*adv_logp)
# pi_loss = -tf.reduce_mean(tf.minimum(ratio * adv_logp, min_adv))
# ### Scheme3: SPPO NO.3: add entropy
# adv_logp = adv_ph - tf.stop_gradient(alpha) * logp_old_ph
# min_adv = tf.where(adv_logp>0, (1+clip_ratio)*adv_logp, (1-clip_ratio)*adv_logp)
# pi_loss = -tf.reduce_mean(tf.minimum(ratio * adv_logp, min_adv))
### Scheme2: SPPO NO.2: add entropy
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) + tf.stop_gradient(alpha) * h)
v_loss = tf.reduce_mean((ret_ph - v)**2) #+(ret_ph - q)**2)/2.0
# 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(
h) # 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=args.pi_lr).minimize(pi_loss + 0.1 * v_loss)
# train_v = MpiAdamOptimizer(learning_rate=args.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):
if args.alpha == 'auto':
sess.run(train_alpha_op, feed_dict=inputs)
_, 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),
Alpha=sess.run(alpha) if args.alpha == 'auto' else alpha)
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, h_t = sess.run(get_action_ops,
feed_dict={x_ph: o.reshape(1, -1)})
# q_t = sess.run(q, feed_dict={x_ph: o.reshape(1,-1), a_ph: a})
# SPPO NO.1: add entropy
# rh = r - args.alpha * logp_t
if args.alpha == 'auto':
rh = r + sess.run(alpha) * h_t
else:
rh = r + alpha * h_t # exact entropy
# save and log
buf.store(o, a, rh, v_t, logp_t)
logger.store(VVals=v_t)
o, r, d, _ = env.step(a[0])
ep_ret += r
ep_len += 1
# d = False if ep_len == max_ep_len else d
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 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('Alpha', 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(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-2,
vf_lr=1e-3,
train_v_iters=80,
lam=0.97,
max_ep_len=1000,
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 = 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)
# 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):
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 td3(env_fn: Callable,
actor_critic: torch.nn.Module = core.MLPActorCritic,
ac_kwargs: Dict = None,
seed: int = 0,
steps_per_epoch: int = 4000,
epochs: int = 2000,
replay_size: int = int(1e6),
gamma: float = 0.99,
polyak: float = 0.995,
pi_lr: Union[Callable, float] = 1e-3,
q_lr: Union[Callable, float] = 1e-3,
batch_size: int = 100,
start_steps: int = 10000,
update_after: int = 1000,
update_every: int = 100,
act_noise: Union[Callable, float] = 0.1,
target_noise: float = 0.2,
noise_clip: float = 0.5,
policy_delay: int = 2,
num_test_episodes: int = 3,
max_ep_len: int = 1000,
logger_kwargs: Dict = None,
save_freq: int = 1,
random_exploration: Union[Callable, float] = 0.0,
save_checkpoint_path: str = None,
load_checkpoint_path: str = None,
load_model_file: str = None):
"""
Twin Delayed Deep Deterministic Policy Gradient (TD3)
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,
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.
``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!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
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 or callable): Learning rate for policy.
q_lr (float or callable): 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 or callable): 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.
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.
random_exploration (float or callable): Probability to randomly select
an action instead of selecting from policy.
save_checkpoint_path (str): Path to save the model. If not set, no model
will be saved
load_checkpoint_path (str): Path to load the model. Cannot be set if
save_model_path is set.
"""
if logger_kwargs is None:
logger_kwargs = dict()
if ac_kwargs is None:
ac_kwargs = dict()
if save_checkpoint_path is not None:
assert load_checkpoint_path is None, "load_model_path cannot be set when save_model_path is already set"
if not os.path.exists(save_checkpoint_path):
print(f"Folder {save_checkpoint_path} does not exist, creating...")
os.makedirs(save_checkpoint_path)
if load_checkpoint_path is not None:
assert load_model_file is None, "load_checkpoint_path cannot be set when load_model_file is already set"
# ------------ Initialisation begin ------------
loaded_state_dict = None
if load_checkpoint_path is not None:
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
loaded_state_dict = load_latest_state_dict(load_checkpoint_path)
logger.epoch_dict = loaded_state_dict['logger_epoch_dict']
q_learning_rate_fn = loaded_state_dict['q_learning_rate_fn']
pi_learning_rate_fn = loaded_state_dict['pi_learning_rate_fn']
epsilon_fn = loaded_state_dict['epsilon_fn']
act_noise_fn = loaded_state_dict['act_noise_fn']
replay_buffer = loaded_state_dict['replay_buffer']
env, test_env = loaded_state_dict['env'], loaded_state_dict['test_env']
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
ac_targ = deepcopy(ac)
ac.load_state_dict(loaded_state_dict['ac'])
ac_targ.load_state_dict(loaded_state_dict['ac_targ'])
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
env.action_space.np_random.set_state(
loaded_state_dict['action_space_state'])
# List of parameters for both Q-networks (save this for convenience)
q_params = itertools.chain(ac.q1.parameters(), ac.q2.parameters())
t_ori = loaded_state_dict['t']
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_learning_rate_fn(t_ori))
pi_optimizer.load_state_dict(loaded_state_dict['pi_optimizer'])
q_optimizer = Adam(q_params, lr=q_learning_rate_fn(t_ori))
q_optimizer.load_state_dict(loaded_state_dict['q_optimizer'])
np.random.set_state(loaded_state_dict['np_rng_state'])
torch.set_rng_state(loaded_state_dict['torch_rng_state'])
else:
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
q_learning_rate_fn = get_schedule_fn(q_lr)
pi_learning_rate_fn = get_schedule_fn(pi_lr)
act_noise_fn = get_schedule_fn(act_noise)
epsilon_fn = get_schedule_fn(random_exploration)
env, test_env = env_fn(), env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
env.action_space.seed(seed)
# Experience buffer
replay_buffer = ReplayBuffer(obs_dim=obs_dim,
act_dim=act_dim,
size=replay_size)
# Create actor-critic module and target networks
if load_model_file is not None:
assert os.path.exists(
load_model_file
), f"Model file path does not exist: {load_model_file}"
ac = torch.load(load_model_file)
else:
ac = actor_critic(env.observation_space, env.action_space,
**ac_kwargs)
ac_targ = deepcopy(ac)
# List of parameters for both Q-networks (save this for convenience)
q_params = itertools.chain(ac.q1.parameters(), ac.q2.parameters())
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_learning_rate_fn(0))
q_optimizer = Adam(q_params, lr=q_learning_rate_fn(0))
t_ori = 0
act_limit = 1.0
# ------------ Initialisation end ------------
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in ac_targ.parameters():
p.requires_grad = False
# 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)
torch.set_printoptions(profile="default")
# Set up function for computing TD3 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():
pi_targ = ac_targ.pi(o2)
# Target policy smoothing
epsilon = torch.randn_like(pi_targ) * target_noise
epsilon = torch.clamp(epsilon, -noise_clip, noise_clip)
a2 = pi_targ + epsilon
a2 = torch.clamp(a2, -act_limit, act_limit)
# 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
# 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
loss_info = dict(Q1Vals=q1.detach().numpy(),
Q2Vals=q2.detach().numpy())
return loss_q, loss_info
# Set up function for computing TD3 pi loss
def compute_loss_pi(data):
o = data['obs']
q1_pi = ac.q1(o, ac.pi(o))
return -q1_pi.mean()
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data, timer):
# First run one gradient descent step for Q1 and Q2
q_optimizer.zero_grad()
loss_q, loss_info = compute_loss_q(data)
loss_q.backward()
q_optimizer.step()
# Record things
logger.store(LossQ=loss_q.item(), **loss_info)
# Possibly update pi and target networks
if timer % policy_delay == 0:
# 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 = 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())
# 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 _ 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)
scaled_action = get_action(o, 0)
o, r, d, _ = test_env.step(
unscale_action(env.action_space, scaled_action))
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()
if loaded_state_dict is not None:
o = loaded_state_dict['o']
ep_ret = loaded_state_dict['ep_ret']
ep_len = loaded_state_dict['ep_len']
else:
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):
t += t_ori
# printMemUsage(f"start of step {t}")
# 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 and np.random.rand() > epsilon_fn(t):
a = get_action(o, act_noise_fn(t))
unscaled_action = unscale_action(env.action_space, a)
else:
unscaled_action = env.action_space.sample()
a = scale_action(env.action_space, unscaled_action)
# Step the env
o2, r, d, _ = env.step(unscaled_action)
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, timer=j)
# End of epoch handling
if (t + 1) % steps_per_epoch == 0:
# Perform LR decay
update_learning_rate(q_optimizer, q_learning_rate_fn(t))
update_learning_rate(pi_optimizer, pi_learning_rate_fn(t))
epoch = (t + 1) // steps_per_epoch
# 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('Time', time.time() - start_time)
logger.dump_tabular()
# Save model and checkpoint
save_checkpoint = False
checkpoint_path = ""
if save_checkpoint_path is not None:
save_checkpoint = True
checkpoint_path = save_checkpoint_path
if load_checkpoint_path is not None:
save_checkpoint = True
checkpoint_path = load_checkpoint_path
if (epoch % save_freq == 0) or (epoch == epochs):
logger.save_state({}, None)
if save_checkpoint:
checkpoint_file = os.path.join(checkpoint_path,
f'save_{epoch}.pt')
torch.save(
{
'ac':
ac.state_dict(),
'ac_targ':
ac_targ.state_dict(),
'replay_buffer':
replay_buffer,
'pi_optimizer':
pi_optimizer.state_dict(),
'q_optimizer':
q_optimizer.state_dict(),
'logger_epoch_dict':
logger.epoch_dict,
'q_learning_rate_fn':
q_learning_rate_fn,
'pi_learning_rate_fn':
pi_learning_rate_fn,
'epsilon_fn':
epsilon_fn,
'act_noise_fn':
act_noise_fn,
'torch_rng_state':
torch.get_rng_state(),
'np_rng_state':
np.random.get_state(),
'action_space_state':
env.action_space.np_random.get_state(),
'env':
env,
'test_env':
test_env,
'ep_ret':
ep_ret,
'ep_len':
ep_len,
'o':
o,
't':
t + 1
}, checkpoint_file)
delete_old_files(checkpoint_path, 10)
def ddpg(env_fn, actor_critic=core.MLPActorCritic, ac_kwargs=dict(), seed=1,
steps_per_epoch=2000, epochs=10000, replay_size=int(1e5), gamma=0.99,
polyak=0.995, pi_lr=1e-4, q_lr=1e-4, batch_size=128, start_steps=2000,
update_after=1000, update_every=1000, act_noise=0.05, num_test_episodes=1,
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)
rospy.init_node('DDPG_Train')
env = env_fn()
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
print(f"[DDPG] obs dim: {obs_dim} action dim: {act_dim}")
# Create actor-critic module and target networks
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
# ac.apply(init_weights)
ac_targ = deepcopy(ac)
ac.eval() # in-active training BN
print(f"[MODEL] Actor_Critic: {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']
# import ipdb
# ipdb.set_trace()
q = ac.q(o, a)
# 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.cpu().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)
def soft_target_update():
# 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):
o = torch.as_tensor(o, dtype=torch.float32)
if o.dim() == 1:
o = o.unsqueeze(0)
a = ac.act(o)[0]
a += noise_scale * np.random.randn(act_dim)
return np.clip(a, env.act_limit_min, env.act_limit_max)
def test_agent():
print("[DDPG] eval......")
for j in range(num_test_episodes):
o, d, ep_ret, ep_len = env.reset(), False, 0, 0
# while not(d or (ep_len == max_ep_len)):
while not(d or (ep_len == 100)):
# Take deterministic actions at test time (noise_scale=0)
a = get_action(o, 0)
print(f"[Eval] a: {a}")
o, r, d, _ = env.step(a)
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).
print(f"O {o[-4]:.3f} {o[-3]:.3f} {o[-2]:.3f} {o[-1]:.3f} ")
if t > start_steps:
# if np.random.rand() > 0.3:
a = get_action(o, act_noise)
# else:
# a = env.action_space.sample()
else:
a = env.action_space.sample()
print(f't {t:7.0f} | a [{a[0]:.3f},{a[1]:.3f}]')
# Step the env
o2, r, d, info = env.step(a)
# print(f"O {o[-4:]} |A {a} |O2 {o2[-4:]} |R {r} |D {d} |Info {info}")
print(f" ------------------> R: {r:.3f}")
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):
env.pause_pedsim()
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0
env.unpause_pedsim()
# Update handling
if t >= update_after and t % update_every == 0:
env.pause_pedsim()
ac.train() # active training BN
ac_targ.train()
if torch.cuda.is_available():
ac.cuda()
ac_targ.cuda()
for _ in range(update_every):
batch = replay_buffer.sample_batch(batch_size)
if torch.cuda.is_available():
for key, value in batch.items():
batch[key] = value.cuda()
update(data=batch)
soft_target_update()
ac.eval()
ac_targ.eval()
if torch.cuda.is_available():
ac.cpu()
ac_targ.cpu()
env.unpause_pedsim()
# 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()
o, d, ep_ret, ep_len = env.reset(), False, 0, 0
sec = time.time() - start_time
elapsed_time = str(datetime.timedelta(seconds=sec)).split('.')[0]
# 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.log_tabular('Time', elapsed_time)
logger.dump_tabular()
def ppo(BASE_DIR,
expert_density,
env_fn,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
steps_per_epoch=1000,
epochs=10,
gamma=0.99,
clip_ratio=0.2,
pi_lr=3e-4,
vf_lr=1e-3,
train_pi_iters=50,
train_v_iters=50,
lam=0.97,
max_ep_len=1000,
target_kl=0.01,
data_n=10):
data = {} # ALL THE DATA
logger_kwargs = setup_logger_kwargs(args.dir_name, data_dir=BASE_DIR)
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
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})
# update rule
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))
policy_distr = Gaussian_Density()
policy = lambda s: np.random.uniform(
-2.0, 2.0, size=env.action_space.shape) # random policy
policy_distr.train(env, policy, args.trajects, args.distr_gamma,
args.iter_length)
density = policy_distr.density()
data[0] = {
'pol_s': policy_distr.num_samples,
'pol_t': policy_distr.num_trajects
}
dist_rewards = []
# repeat REIL for given number of rounds
for i in range(args.rounds):
message = "\nRound {} out of {}\n".format(i + 1, args.rounds)
reward = lambda s: expert_density(s) / (density(s) + args.eps)
dist_rewards.append(reward)
start_time = time.time()
o, old_r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
r = reward(o) # custom reward
# 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, old_r, d, _ = env.step(a[0])
r = reward(o)
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 = old_r if d else sess.run(
v, feed_dict={x_ph: o.reshape(1, -1)})
last_val = reward(o)
buf.finish_path(last_val)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, old_r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
r = reward(o)
# store model!
if (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()
print(message)
policy = lambda state: sess.run(
get_action_ops, feed_dict={x_ph: state.reshape(1, -1)})[0][0]
data[i] = {
'pol_s': policy_distr.num_samples,
'pol_t': policy_distr.num_trajects
}
data[i]['rewards'] = evaluate_reward(env, policy, data_n)
if i != args.rounds - 1:
policy_distr.train(env, policy, args.trajects, args.distr_gamma,
args.iter_length)
density = policy_distr.density()
return data, dist_rewards
def ppo(env_fn,
GUI=True,
actor_critic=my_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,
on_policy=True,
prev_epochs=0):
"""
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.
GUI : Whether or not display GUI during training.
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.) 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.
"""
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
seed += 10000 * proc_id()
tf.set_random_seed(seed)
np.random.seed(seed)
if GUI:
env = env_fn("GUI", prev_epochs)
else:
env = env_fn("DIRECT", prev_epochs)
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
sess = tf.Session()
# Inputs to computation graph
x_ph, a_ph = core.placeholders_from_spaces(env.observation_space,
env.action_space)
# Main outputs from computation graph
pi, logp, logp_pi, v, mu, log_std = actor_critic(x_ph, a_ph, **ac_kwargs)
# if load_path==None:
# # Inputs to computation graph
# x_ph, a_ph = core.placeholders_from_spaces(env.observation_space, env.action_space)
# # Main outputs from computation graph
# pi, logp, logp_pi, v = actor_critic(x_ph, a_ph, **ac_kwargs)
# else:
# fname = osp.join(load_path, 'tf1_save')
# print('\n\nLoading old model from %s.\n\n' % fname)
#
# # load the things!
# model = restore_tf_graph(sess, fname)
# x_ph, a_ph = model['x'], model['a']
# pi, logp, logp_pi, v = model['pi'], model['logp'], model['logp_pi'], model['v']
# Calculated through one epoch, assigned by buf's methods
adv_ph, ret_ph, logp_old_ph = core.placeholders(None, None, None)
# 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.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,
'a': a_ph
},
outputs={
'pi': pi,
'v': v,
'logp': logp,
'logp_pi': logp_pi
})
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)
# lllogp, mmmu, llog_std = sess.run([logp, mu, log_std], feed_dict=inputs)
# logp is basically the same as logp_old_ph, the error starts from 1e-6,
# and this error is a little strange...
# 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):
last_noise_time = 0.0
noise = np.zeros(12)
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)}) # CHANGE THE feed_dict HERE!
# aa = a.copy()
# if 2.0 < env.t < 4.0:
# # on_policy = False
# if env.t - last_noise_time > 0.1:
# noise = np.random.uniform(-0.5 * np.pi, 0.5 * np.pi, 12)
# last_noise_time += 0.1
# a += noise
# logp_t = sess.run(logp, feed_dict={x_ph: o.reshape(1, -1), a_ph: a})
# else:
# # on_policy = True
# pass
# print("time:", env.t, a-aa)
if not on_policy:
a = np.array([get_action_from_target_policy(env.t)])
logp_t = sess.run(logp,
feed_dict={
x_ph: o.reshape(1, -1),
a_ph: a
})
env.history_buffer['last_action'] = a[0]
for i in range(
25): # Change the frequency of control from 500Hz to 20Hz
o2, r, d, o2_dict = env.step(a[0])
ep_ret += r
ep_len += 1
# save and log
buf.store(o, a, r, v_t, logp_t)
logger.store(VVals=v_t)
# Update obs (critical!)
o = o2
# print(ep_len, d)
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
if d:
last_val = 0
# print(o2_dict['position'])
# print(np.alltrue(o2_dict['position'][i] < -1 for i in [1, 4, 7, 10]) is True)
# print(np.alltrue([o2_dict['position'][i] < -1 for i in [1, 4, 7, 10]]))
# print("I did it!!!")
else:
# last_val = sess.run(v, feed_dict={x_ph: o.reshape(1, -1)})
last_val = 0
buf.finish_path(last_val)
print(ep_ret)
# logger.store(EpRet=ep_ret+last_val, EpLen=ep_len)
# if terminal:
# o, ep_ret, ep_len = env.reset(), 0, 0
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
last_noise_time = 0.0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform PPO update!
update()
env.addEpoch()
# 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() # show the log
if time.ctime()[-13:-11] == '09':
break
env.close()
class DeterministicLearner:
""" Learner for training Agents with deterministic policies,
and thus have different behavior during training and testing """
def __init__(self,
agent,
env,
steps_per_epoch=5000,
epochs=50,
seed=0,
max_ep_len=1000,
start_steps=10000,
replay_size=int(1e6),
batch_size=100,
n_test_episodes=10,
output_dir=None,
output_fname='progress.txt',
exp_name=None):
self.epoch_len, self.n_epochs = steps_per_epoch, epochs
self.max_ep_len, self.start_steps = max_ep_len, start_steps
self.n_test_episodes = n_test_episodes
self.logger = EpochLogger(output_dir=output_dir,
output_fname=output_fname,
exp_name=exp_name)
print('locals')
for key, val in locals().items():
print('{}: {}'.format(key, len(str(val))))
# self.logger.save_config(locals())
self.env, self.agent = env, agent
self.buffer = OffPolicyBuffer(buffer_size=replay_size,
epoch_size=steps_per_epoch,
batch_size=batch_size)
saver_kwargs = agent.build_graph(env.observation_space,
env.action_space)
self.logger.setup_tf_saver(**saver_kwargs)
var_counts = tuple(
tf_utils.trainable_count(scope) for scope in ['pi', 'q'])
self.logger.log('\nNumber of parameters: \t pi: %d, \t q: %d\n' %
var_counts)
np.random.seed(seed)
tf.set_random_seed(seed)
def episode_step(self,
obs,
rew,
is_term,
ep_len,
ep_ret,
epoch_ctr,
testing=False):
""" take a single step in the episode """
# environment variables to store in buffer
env_to_buffer = dict(obs=obs, rew=rew, is_term=is_term)
# Take agent step, return values to store in buffer, and in logs
act = self.agent.step(obs, testing=testing)
if not testing:
self.buffer.store({**env_to_buffer, 'act': act})
epoch_ctr += 1
ep_len += 1
ep_ret += rew
obs, rew, is_term, _ = self.env.step(act)
return obs, rew, is_term, ep_len, ep_ret, epoch_ctr
def play_episode(self, epoch_ctr=0, testing=False):
""" play out an episode until one of these things happens:
1. episode ends
2. max episode length is reached
3. end of epoch is reached """
obs = self.env.reset()
rew, ep_len, ep_ret, is_term_state = 0, 0, 0, False
while ((ep_len < self.max_ep_len) and (not is_term_state)
and (epoch_ctr < self.epoch_len)):
step_ret = self.episode_step(obs,
rew,
is_term_state,
ep_len,
ep_ret,
epoch_ctr,
testing=testing)
obs, rew, is_term_state, ep_len, ep_ret, epoch_ctr = step_ret
ep_ret += rew # important! add the last reward to the return!
log_prefix = 'Test' if testing else ''
if (is_term_state) or (ep_len >= self.max_ep_len):
self.logger.store(**{
log_prefix + 'EpRet': ep_ret,
log_prefix + 'EpLen': ep_len
})
if not testing:
self.buffer.finish_path(last_obs=obs)
return ep_len, ep_ret, epoch_ctr
def train_episode(self, ep_len):
""" train agent at the end of episode """
batches = self.buffer.batches(n_batches=ep_len)
for train_iter, batch in enumerate(batches):
to_logger = self.agent.train(train_iter, batch)
self.logger.store(**to_logger)
def run_epoch(self):
""" run epoch of training + evaluation """
epoch_ctr = 0
while epoch_ctr < self.epoch_len:
ep_len, _, epoch_ctr = self.play_episode(epoch_ctr=epoch_ctr,
testing=False)
self.train_episode(ep_len)
self.test_epoch(self.n_test_episodes)
def test_epoch(self, n_test_episodes):
""" perform testing for an epoch """
for _ in range(n_test_episodes):
self.play_episode(0, testing=True)
def learn(self):
""" Train the agent over n_epochs """
for epoch in range(self.n_epochs):
start_time = time.time()
self.run_epoch()
self.log_epoch(epoch, start_time)
self.logger.save_state({'env': self.env}, None)
self.agent.sess.close()
def log_epoch(self, epoch, start_time):
""" Log info about epoch """
self.logger.log_tabular('Epoch', epoch)
self.logger.log_tabular('EpRet', with_min_and_max=True)
self.logger.log_tabular('EpLen', average_only=True)
self.logger.log_tabular('TestEpRet', with_min_and_max=True)
self.logger.log_tabular('TestEpLen', average_only=True)
self.logger.log_tabular('TotalEnvInteracts',
(epoch + 1) * self.epoch_len)
self.logger.log_tabular('Time', time.time() - start_time)
for column_name, kwargs in self.agent.log_tabular_kwargs.items():
self.logger.log_tabular(column_name, **kwargs)
self.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):
# Special function to avoid certain slowdowns from PyTorch + MPI combination
setup_pytorch_for_mpi()
# Setup 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 parameters across processes
sync_params(ac)
# Count variables
var_counts = tuple(
core.count_variables(module) for module in [ac.pi, ac.v])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts)
# Set up experiment 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 a 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, log_p = ac.pi(obs, act)
ratio = torch.exp(log_p - 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 Information
approx_kl = (logp_old - log_p).mean().item()
ent = pi.entropy().mean().item()
clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio)
clip_fraction = torch.as_tensor(clipped,
dtype=torch.float32).mean().item()
pi_info = dict(kl=approx_kl, ent=ent, cf=clip_fraction)
return loss_pi, pi_info
# Setup function for computing value loss
def compute_loss_v(data):
obs, ret = data['obs'], data['ret']
return ((ac.v(obs) - ret)**2).mean()
# Setup optimizers for policy and value functions
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()
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)
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)
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))
# Prepare for interaction with the 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 time_out
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)
# 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 ddpg(env_name,
partially_observable=False,
pomdp_type='remove_velocity',
flicker_prob=0.2,
random_noise_sigma=0.1,
random_sensor_missing_prob=0.1,
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_name : A function which creates a copy of the environment.
The environment must satisfy the OpenAI Gym API.
partially_observable:
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)
# Wrapper environment if using POMDP
if partially_observable:
env = POMDPWrapper(env_name, pomdp_type, flicker_prob,
random_noise_sigma, random_sensor_missing_prob)
test_env = POMDPWrapper(env_name, pomdp_type, flicker_prob,
random_noise_sigma, random_sensor_missing_prob)
else:
env, test_env = gym.make(env_name), gym.make(env_name)
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]
# Create actor-critic module and target networks
ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs)
ac_targ = deepcopy(ac)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ac.to(device)
ac_targ.to(device)
# 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, batch_hist, t):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
# batch_hist['pred_q_hist']
# batch_hist['targ_q_hist']
# batch_hist['targ_next_q_hist']
# batch_hist['sampled_time_hist']
q = ac.q(o, a)
# Bellman backup for Q function
with torch.no_grad():
q_pi_targ = ac_targ.q(o2, ac_targ.pi(o2))
# if t < 50000:
# Average over historically predicted q-values
window_size = 10
mean_targ_next_q_hist = []
tuned_indicator = np.zeros(q_pi_targ.shape)
batch_change_rate = []
for i in range(len(batch_hist['targ_next_q_hist'])):
tmp_batch_hist = np.asarray(batch_hist['targ_next_q_hist'][i])
tmp_batch_hist = np.append(
tmp_batch_hist, q_pi_targ[i].item()) # add new prediction
change_rate = tmp_batch_hist[1:] - tmp_batch_hist[:-1]
if len(tmp_batch_hist) == 1:
batch_change_rate.append(None)
else:
batch_change_rate.append(change_rate[-1])
batch_change_rate = np.asarray(batch_change_rate).astype(float)
not_nan_idxs = np.argwhere(~np.isnan(batch_change_rate))
sorted_not_nan_idxs = np.argsort(
batch_change_rate[not_nan_idxs.flatten()])
threshold_percentile = 75 # 25, 50, 75
if len(sorted_not_nan_idxs) != 0:
threshold = np.percentile(
batch_change_rate[not_nan_idxs[sorted_not_nan_idxs]],
threshold_percentile)
if threshold < 0:
threshold = 0
else:
threshold = 1
# threshold = 1 # thresold=1 works for HalfCheetahBulletEnv-v0
# New threshold
for i in range(len(batch_hist['targ_next_q_hist'])):
tmp_batch_hist = np.asarray(batch_hist['targ_next_q_hist'][i])
tmp_batch_hist = np.append(
tmp_batch_hist, q_pi_targ[i].item()) # add new prediction
change_rate = tmp_batch_hist[1:] - tmp_batch_hist[:-1]
if len(tmp_batch_hist) == 1:
avg_window = tmp_batch_hist[-1]
else:
if change_rate[-1] > threshold:
avg_window = tmp_batch_hist[-2] + threshold
# avg_window = tmp_batch_hist[-2]
tuned_indicator[i] = 1
else:
avg_window = tmp_batch_hist[-1]
mean_targ_next_q_hist.append(avg_window)
# print(batch_change_rate[not_nan_idxs[sorted_not_nan_idxs]])
# import pdb; pdb.set_trace()
# if t>10000:
# import pdb; pdb.set_trace()
avg_q_pi_targ = torch.as_tensor(mean_targ_next_q_hist,
dtype=torch.float32).to(device)
# else:
# avg_q_pi_targ = q_pi_targ
# tuned_indicator = np.zeros(q_pi_targ.shape)
backup = r + gamma * (1 - d) * avg_q_pi_targ
# backup = r + gamma * (1 - d) * q_pi_targ
# import pdb;
# pdb.set_trace()
# MSE loss against Bellman backup
loss_q = ((q - backup)**2).mean()
# Useful info for logging
loss_info = dict(QVals=q.cpu().detach().numpy(),
TunedNum=tuned_indicator.sum(),
THLD=threshold)
return loss_q, loss_info, q, backup, avg_q_pi_targ, tuned_indicator # Crucial log shapped q_pi_targ to history
# 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, batch_hist, t):
# First run one gradient descent step for Q.
q_optimizer.zero_grad()
loss_q, loss_info, q, backup, q_pi_targ, tuned_indicator = compute_loss_q(
data, batch_hist, t)
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. (Common choice: 0.995)
# # TODO: remove later
# polyak = 0.4
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)
return q.cpu().detach().numpy(), backup.cpu().detach().numpy(
), q_pi_targ.cpu().detach().numpy(), tuned_indicator
def get_action(o, noise_scale):
a = ac.act(torch.as_tensor(o, dtype=torch.float32).to(device))
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):
sample_type = 'pseudo_random' # 'pseudo_random' genuine_random
batch, batch_hist, batch_idxs = replay_buffer.sample_batch(
batch_size, device=device, sample_type=sample_type)
q, backup, q_pi_targ, tuned_indicator = update(
data=batch, batch_hist=batch_hist, t=t)
replay_buffer.add_sample_hist(batch_idxs, q, backup, q_pi_targ,
tuned_indicator, t)
# End of epoch handling
if (t + 1) % steps_per_epoch == 0:
epoch = (t + 1) // steps_per_epoch
# # Save model
# fpath = osp.join(logger.output_dir, 'pyt_save')
# os.makedirs(fpath, exist_ok=True)
# context_fname = 'checkpoint-context-' + (
# 'Step-%d' % t if t is not None else '') + '.pt'
# context_fname = osp.join(fpath, context_fname)
# if (epoch % save_freq == 0) or (epoch == epochs):
# logger.save_state({'env': env}, None)
# torch.save({'replay_buffer': replay_buffer}, context_fname)
# 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('TunedNum', with_min_and_max=True)
logger.log_tabular('THLD', with_min_and_max=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
def ppo(env_fn,
ref_func=None,
actor_critic=core.mlp_actor_critic,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=500,
epochs=10000,
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=500,
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)
t_a_ph = core.placeholder_from_space(env.action_space)
ret_ph = core.placeholder(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, t_a_ph, ret_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, 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)
# dagger objectives
pi_loss = tf.reduce_mean(tf.square(pi - t_a_ph))
v_loss = tf.reduce_mean((ret_ph - v)**2)
# 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 = sess.run([pi_loss, v_loss], feed_dict=inputs)
# Training
for i in range(train_pi_iters):
sess.run(train_pi, feed_dict=inputs)
for _ in range(train_v_iters):
sess.run(train_v, feed_dict=inputs)
# Log changes from update
pi_l_new, v_l_new = sess.run([pi_loss, v_loss], feed_dict=inputs)
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
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(1, epochs + 1, 1):
for t in range(local_steps_per_epoch):
a_s, v_t, logp_t = sess.run(
get_action_ops, feed_dict={x_ph: np.array(o).reshape(1, -1)})
a = a_s[0]
ref_a = call_mpc(env, ref_func)
if (epoch < 100):
a = ref_a
# save and log
buf.store(o, a, ref_a, r)
o, r, d, _ = env.step(a)
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
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({}, 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('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('Time', time.time() - start_time)
logger.dump_tabular()
def gail(env_fn,traj_dir, actor_critic=core.mlp_actor_critic_add, ac_kwargs=dict(),d_hidden_size =64,d_batch_size = 64,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=40, train_v_iters=40, lam=0.97, max_ep_len=4000,beta =1e-4,
target_kl=0.01, logger_kwargs=dict(), save_freq=100,
r_env_ratio=0,gail_ratio =1, d_itr =20, reward_type = 'negative',
pretrain_bc_itr =0):
"""
additional args
d_hidden_size : hidden layer size of Discriminator
d_batch_size : Discriminator's batch size
r_env_ratio,gail_ratio : the weight of rewards from envirionment and gail .Total reward = gail_ratio *rew_gail+r_env_ratio* rew_from_environment
d_itr : The number of iteration of update discriminater
reward_type : GAIL reward has three type ['negative','positive', 'AIRL']
trj_num :the number of trajectory for
pretrain_bc_itr: the number of iteration of pretraining by behavior cloeing
"""
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
D=Discriminator(env,hidden_size = d_hidden_size,reward_type =reward_type)
e_obs = np.loadtxt(traj_dir + '/observations.csv',delimiter=',')
e_act = np.loadtxt(traj_dir + '/actions.csv',delimiter= ',')#Demo treajectory
Sibuffer =SIBuffer(obs_dim, act_dim, e_obs,e_act,trj_num= 0, max_size =None)#!sibuf
assert e_obs.shape[1:] == obs_dim
# 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,pi_std, entropy, 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)
#buf_gail = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)#add buffer with TRgail rewards
# 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))- beta*entropy
v_loss = tf.reduce_mean((ret_ph - v)**2)#ret_phには累積報酬のバッファが入る
# 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()
BC = BehavioralCloning(sess,pi,logp,x_ph,a_ph)
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
# Sync params across processes
# 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())}#all_phsは各バッファーに対応するプレースホルダー辞書
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent], feed_dict=inputs)
# Training#ここも変える必要あり? おそらく変えなくて良い
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:#更新時のklが想定の1.5倍大きいとログをだしてtrainループを着る
logger.log('Early stopping at step %d due to reaching max kl.'%i)
break
logger.store(StopIter=i)
for _ in range(train_v_iters):#ｖの更新
sess.run(train_v, feed_dict=inputs)
# Log changes from update（新しいロスの計算）
pi_l_new, v_l_new, kl, cf = sess.run([pi_loss, v_loss, approx_kl, clipfrac], feed_dict=inputs)
std, std_ent = sess.run([pi_std,entropy],feed_dict = inputs)
logger.store(LossPi=pi_l_old, LossV=v_l_old,
KL=kl, Entropy=std_ent, ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old),#更新での改善量
DeltaLossV=(v_l_new - v_l_old),
Std = std)
start_time = time.time()
o, r, d, ep_ret_task,ep_ret_gail, ep_len = env.reset(), 0, False, 0,0 , 0
if pretrain_bc_itr>0:
BC.learn(Sibuffer.expert_obs,Sibuffer.expert_act ,max_itr =pretrain_bc_itr)
# 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])
buf.store_rew(r)
'''
if t <150:
env.render()
time.sleep(0.03)
'''
ep_ret_task += r
ep_len += 1
terminal = d or (ep_len == max_ep_len)
if terminal or (t==local_steps_per_epoch-1):
if d:# if trajectory didn't reach terminal state, bootstrap value target
last_val = r
else:
last_val = sess.run(v, feed_dict={x_ph: o.reshape(1,-1)})#v_last=...だったけどこれで良さげ
buf.store_rew(last_val)#if its terminal ,nothing change and if its maxitr last_val is use
buf.finish_path()
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(EpRet=ep_ret_task, EpLen=ep_len)#,EpRet_Sum =ep_ret_sum,EpRet_Gail =ep_ret_gail)
o, r, d, ep_ret_task,ep_ret_sum,ep_ret_gail, ep_len = env.reset(), 0, False, 0, 0, 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs-1):
logger.save_state({'env': env}, epoch)
agent_obs , agent_act = buf.obs_buf, buf.act_buf
d_batch_size = d_batch_size#or len(agent_obs)//d_itr #update discreminator
for _t in range(d_itr):
e_obs_batch ,e_act_batch =Sibuffer.get_random_batch(d_batch_size)
a_obs_batch =sample_batch(agent_obs,batch_size = d_batch_size)
a_act_batch= sample_batch(agent_act,batch_size = d_batch_size)
D.train(sess, e_obs_batch,e_act_batch , a_obs_batch,a_act_batch )
js_d = D.get_js_div(sess,Sibuffer.main_obs_buf,Sibuffer.main_act_buf,agent_obs,agent_act)
#---------------get_gail_reward------------------------------
rew_gail=D.get_reward(sess,agent_obs, agent_act).ravel()
buf.rew_buf = gail_ratio *rew_gail+r_env_ratio*buf.rew_buf
for path_slice in buf.slicelist[:-1]:
ep_ret_gail = rew_gail[path_slice].sum()
ep_ret_sum = buf.rew_buf[path_slice].sum()
logger.store(EpRet_Sum=ep_ret_sum,EpRet_Gail=ep_ret_gail)
buf.culculate_adv_buf()
# -------------Perform PPO update!--------------------
update()
logger.store(JS=js_d)
# Log info about epoch
#if epoch%10 == 0:#logger print each 10 epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpRet_Sum', average_only=True)
logger.log_tabular('EpRet_Gail', 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('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time()-start_time)
logger.log_tabular('Std', average_only=True)
logger.log_tabular('JS', average_only=True)
#logger.log_tabular('JS_Ratio', average_only=True)
logger.dump_tabular()
def ppo(env_fn, actor_critic=core_2.mlp_actor_critic, beta=1, 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() # game environment
obs_dim = env.observation_space.shape # get the observe dimension from environment
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
#print(env.action_space)
x_ph, a_ph = core_2.placeholders_from_spaces(env.observation_space, env.action_space) # 构建神经网络的时候，a_ph还没有
adv_ph, ret_ph, logp_old_ph, log_old_ph_all = core_2.placeholders(None, None, None, 18)
#print(logp_old_ph)
#print(log_old_ph_all)
# Main outputs from computation graph
pi, logp, logp_pi, v, logp_all = actor_critic(x_ph, a_ph, **ac_kwargs) # 目前这里的状态和action都还是放的placeholder
# 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, log_old_ph_all]
# Every step, get: action, value, and logprob # 每一步都需要得到action（这里的pi似乎表示action）
get_action_ops = [pi, v, logp_pi, logp_all]
# 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_2.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)
#print((tf.exp(log_old_ph_all) * (logp - logp_old_ph)))
kl = tf.reduce_mean(tf.multiply(tf.exp(log_old_ph_all),tf.transpose([logp - logp_old_ph])))
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)) # 两部分的loss
pi_loss = -tf.reduce_mean(ratio * adv_ph - beta * kl)
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
# 同步参数
sess.run(sync_all_params())
# Setup model saving
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs={'pi': pi, 'v': v})
start_time = time.time()
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# 主循环
# 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, logp_all = sess.run(get_action_ops, feed_dict={x_ph: o.reshape(1, -1)})
# save and log
# 把数据放进 buffer pool 里
buf.store(o, a, r, v_t, logp_t, logp_all)
logger.store(VVals=v_t)
# o 应该代表observation
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 PPO update!
# 打完一局游戏，执行一次更新
#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):
_, kld = sess.run([train_pi, kl], feed_dict=inputs)
kld = mpi_avg(kld)
if kld > 1.5 * target_kl:
beta = 2 * beta
if kld < target_kl / 1.5:
beta = beta / 2
# logger.log('Early stopping at step %d due to reaching max kl.' % i)
# break
logger.store(StopIter=i)
# 上部分的train是policy，这部分是值函数
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))
# 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 td3(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, target_noise=0.2,
noise_clip=0.5, policy_delay=2, num_test_episodes=10, max_ep_len=1000,
logger_kwargs=dict(), save_freq=1):
"""
Twin Delayed Deep Deterministic Policy Gradient (TD3)
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,
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.
``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!)
=========== ================ ======================================
ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object
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.
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.)
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.
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)
#=========================================================================#
# #
# All of your code goes in the space below. #
# #
#=========================================================================#
# Set up function for computing TD3 Q-losses
def compute_loss_q(data):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data['obs2'], data['done']
# Compute target actions
a_next = ac_targ.pi(torch.as_tensor(o2, dtype=torch.float32))
a_next += torch.clamp(target_noise * torch.randn(act_dim), -noise_clip, noise_clip)
a_next = torch.clamp(a_next, -act_limit, act_limit)
# Compute targets
q1 = ac_targ.q1(o2, a_next)
q2 = ac_targ.q2(o2, a_next)
y = r + gamma * (1 - d) * torch.min(q1, q2)
# Loss function
loss_q1 = ((ac.q1(o, a) - y) ** 2).mean()
loss_q2 = ((ac.q2(o, a) - y) ** 2).mean()
loss_q = loss_q1 + loss_q2
# Useful info for logging
loss_info = dict(Q1Vals=q1.detach().numpy(),
Q2Vals=q2.detach().numpy())
return loss_q, loss_info
# Set up function for computing TD3 pi loss
def compute_loss_pi(data):
o = torch.as_tensor(data['obs'], dtype=torch.float32)
loss_pi = -ac.q1(o, ac.pi(o)).mean() # Gradient ascent
return loss_pi
#=========================================================================#
# #
# All of your code goes in the space above. #
# #
#=========================================================================#
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr)
q_optimizer = Adam(q_params, lr=q_lr)
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data, timer):
# First run one gradient descent step for Q1 and Q2
q_optimizer.zero_grad()
loss_q, loss_info = compute_loss_q(data)
loss_q.backward()
q_optimizer.step()
# Record things
logger.store(LossQ=loss_q.item(), **loss_info)
# Possibly update pi and target networks
if timer % policy_delay == 0:
# 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 = 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())
# 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 j in range(update_every):
batch = replay_buffer.sample_batch(batch_size)
update(data=batch, timer=j)
# 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('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('Time', time.time()-start_time)
logger.dump_tabular()
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):
"""
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: 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.) 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.
"""
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_dims = env.action_space #[ choice.shape for choice in env.action_space.values() ]
#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.placeholder(None), core.placeholder(
None), {}
for k in env.action_space:
logp_old_ph[k] = core.placeholder(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_dims, 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, min_adv, pi_loss = {}, {}, {}
for k in env.action_space:
ratio[k] = tf.exp(logp[k] - logp_old_ph[k]) # pi(a|s) / pi_old(a|s)
min_adv[k] = tf.where(adv_ph > 0, (1 + clip_ratio) * adv_ph,
(1 - clip_ratio) * adv_ph)
pi_loss[k] = -tf.reduce_mean(tf.minimum(ratio[k] * adv_ph, min_adv[k]))
v_loss = tf.reduce_mean((ret_ph - v)**2)
# Info (useful to watch during learning)
approx_kl, approx_ent, clipped, clipfrac = {}, {}, {}, {}
for k in env.action_space:
approx_kl[k] = tf.reduce_mean(
logp_old_ph[k] -
logp[k]) # a sample estimate for KL-divergence, easy to compute
approx_ent[k] = tf.reduce_mean(
-logp[k]) # a sample estimate for entropy, also easy to compute
clipped[k] = tf.logical_or(ratio[k] > (1 + clip_ratio), ratio[k] <
(1 - clip_ratio))
clipfrac[k] = tf.reduce_mean(tf.cast(clipped[k], tf.float32))
pi_loss_sum = tf.reduce_sum(list(pi_loss.values()))
# Optimizers
train_pi = MpiAdamOptimizer(learning_rate=pi_lr).minimize(pi_loss_sum)
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
save_outputs = {'v': v}
for k in env.action_space:
save_outputs['pi_' + k] = pi[k]
logger.setup_tf_saver(sess, inputs={'x': x_ph}, outputs=save_outputs)
def update():
inputs = {}
for k, v in zip(all_phs, buf.get()):
if type(k) is not dict:
inputs[k] = v
else:
for k_, v_ in zip(k.values(), v.values()):
inputs[k_] = v_
pi_l_old, v_l_old, ent = sess.run([pi_loss_sum, 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)
for k in kl:
kl[k] = mpi_avg(kl[k])
if max(list(kl.values())) > 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_sum, v_loss, approx_kl, clipfrac], feed_dict=inputs)
sum_dict = lambda x: x if type(x) is not dict else np.sum(
list(x.values()))
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=sum_dict(kl),
Entropy=sum_dict(ent),
ClipFrac=sum_dict(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)})
o2, r, d, _ = env.step(**a)
env.render() #force_realtime=True)
ep_ret += r
#print ("frame_return: %.4f sofar_EpRet: %.4f" % (r, ep_ret))
ep_len += 1
# save and log
buf.store(o, a, r, v_t, logp_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)
print("EpRet:", ep_ret)
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 eglu(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=256,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1,
eps=0.2,
n_explore=32,
device='cuda'):
device = torch.device(device)
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)
# 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,
device=device)
# 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().cpu().numpy(),
Q2Vals=q2.detach().cpu().numpy())
return loss_q, q_info
# Set up function for computing EGL mean-gradient-losses
def compute_loss_g(data):
o, a1, r, o_tag, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
a2 = ball_explore(a1, n_explore, eps)
a2 = a2.view(n_explore * len(r), act_dim)
o_expand = repeat_and_reshape(o, n_explore)
# Bellman backup for Q functions
with torch.no_grad():
q1 = ac.q1(o_expand, a2)
q2 = ac.q2(o_expand, a2)
q_dither = torch.min(q1, q2)
# Target actions come from *current* policy
a_tag, logp_a_tag = ac.pi(o_tag)
# Target Q-values
q1_pi_targ = ac_targ.q1(o_tag, a_tag)
q2_pi_targ = ac_targ.q2(o_tag, a_tag)
q_pi_targ = torch.min(q1_pi_targ, q2_pi_targ)
q_anchor = r + gamma * (1 - d) * (q_pi_targ - alpha * logp_a_tag)
q_anchor = repeat_and_reshape(q_anchor, n_explore).squeeze(-1)
a1_in = autograd.Variable(a1.data, requires_grad=True)
q1 = ac.q1(o, a1_in)
q2 = ac.q2(o, a1_in)
qa = torch.min(q1, q2).unsqueeze(-1)
geps = autograd.grad(outputs=qa,
inputs=a1_in,
grad_outputs=torch.cuda.FloatTensor(
qa.size()).fill_(1.),
create_graph=False,
retain_graph=True,
only_inputs=True)[0]
geps = repeat_and_reshape(geps, n_explore)
a1 = repeat_and_reshape(a1, n_explore)
geps = (geps * (a2 - a1)).sum(-1)
# l1 loss against Bellman backup
loss_g = F.smooth_l1_loss(geps, q_dither - q_anchor)
# Useful info for logging
g_info = dict(GVals=geps.flatten().detach().cpu().numpy())
return loss_g, g_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().cpu().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()
# Next run one gradient descent step for the mean-gradient
loss_g, g_info = compute_loss_g(data)
# Record things
logger.store(LossG=loss_g.item(), **g_info)
q_optimizer.zero_grad()
loss_q, q_info = compute_loss_q(data)
# Record things
logger.store(LossQ=loss_q.item(), **q_info)
loss_q = loss_q + loss_g
loss_q.backward()
q_optimizer.step()
# Freeze Q-networks so you don't waste computational effort
# computing gradients for them during the policy learning step.
for p in ac.geps.parameters():
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 ac.geps.parameters():
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, device=device),
deterministic)
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
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 tqdm(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)
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 sigail(env_fn,
traj_dir,
actor_critic=core.mlp_actor_critic_add,
ac_kwargs=dict(),
d_hidden_size=64,
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=40,
train_v_iters=40,
lam=0.97,
max_ep_len=4000,
beta=1e-4,
target_kl=0.01,
logger_kwargs=dict(),
save_freq=100,
r_env_ratio=0,
d_itr=20,
reward_type='negative',
trj_num=20,
buf_size=1000,
si_update_ratio=0.02,
js_smooth=5,
buf_update_type='random',
pretrain_bc_itr=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) | 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
D = Discriminator(env, hidden_size=d_hidden_size,
reward_type=reward_type) #!add Discriminator object
D_js_m = JS_div_machine(env, hidden_size=d_hidden_size)
e_obs = np.zeros((buf_size, obs_dim[0]))
e_act = np.zeros((buf_size, act_dim[0]))
Sibuffer = SIBuffer(obs_dim,
act_dim,
e_obs,
e_act,
trj_num=trj_num,
max_size=buf_size,
js_smooth_num=js_smooth) #!sibuf
trj_full = False
assert e_obs.shape[1:] == obs_dim
# 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, pi_std, entropy, 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)
#buf_gail = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)#add buffer with TRgail rewards
# 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)) - beta * entropy #add entropy
v_loss = tf.reduce_mean((ret_ph - v)**2) #ret_phには累積報酬のバッファが入る
# 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()
BC = BehavioralCloning(sess, pi, logp, x_ph, a_ph)
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
# Sync params across processes
# 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())
} #all_phsは各バッファーに対応するプレースホルダー辞書
pi_l_old, v_l_old, ent = sess.run([pi_loss, v_loss, approx_ent],
feed_dict=inputs)
# Training#ここも変える必要あり? おそらく変えなくて良い
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: #更新時のklが想定の1.5倍大きいとログをだしてtrainループを着る
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
logger.store(StopIter=i)
for _ in range(train_v_iters): #ｖの更新
sess.run(train_v, feed_dict=inputs)
# Log changes from update（新しいロスの計算）
pi_l_new, v_l_new, kl, cf = sess.run(
[pi_loss, v_loss, approx_kl, clipfrac], feed_dict=inputs)
std, std_ent = sess.run([pi_std, entropy], feed_dict=inputs)
logger.store(
LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=std_ent,
ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old), #更新での改善量
DeltaLossV=(v_l_new - v_l_old),
Std=std)
start_time = time.time()
o, r, d, ep_ret_task, ep_ret_gail, ep_len = env.reset(), 0, False, 0, 0, 0
if pretrain_bc_itr > 0:
BC.learn(Sibuffer.expert_obs,
Sibuffer.expert_act,
max_itr=pretrain_bc_itr)
# 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])
'''
if t <150:
env.render()
time.sleep(0.03)
'''
ep_ret_task += 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)
'''
#!add discriminator train
'''#終端も加えるならアリッチャあり
o_reshape = o.reshape(core.combined_shape(1,obs_dim))
a_reshape = a.reshape(core.combined_shape(1,act_dim))
agent_obs = np.append(buf.obs_buf[buf.path_slice()],o_reshape,axis = 0)#!o を(obspace,)→(1,obspace)に変換してからアペンド
agent_act = np.append(buf.act_buf[buf.path_slice()],a_reshape,axis = 0)#終端での状態行動対も加えてDを学習
'''
agent_obs = buf.obs_buf[buf.path_slice()]
agent_act = buf.act_buf[buf.path_slice()]
#D.train(sess,e_obs,e_act ,agent_obs,agent_act)
#↓buf.r_gail_buf[slice(buf.path_start_idx+1, buf.ptr+2)] = D.get_reward_buf(sess,agent_obs, agent_act).ravel()#状態行動対の結果としての報酬をbufferに追加（報酬は一個ずれる）
if trj_full:
gail_r = 1
else:
gail_r = 0
rew_gail = gail_r * D.get_reward(
sess, agent_obs,
agent_act).ravel() #状態行動対の結果としての報酬をbufferに追加（報酬は一個ずれる）
ep_ret_gail += rew_gail.sum() #!before gail_ratio
ep_ret_sum = r_env_ratio * ep_ret_task + ep_ret_gail
rew_gail_head = rew_gail[:-1]
last_val_gail = rew_gail[-1]
buf.rew_buf[slice(
buf.path_start_idx + 1,
buf.ptr)] = rew_gail_head + r_env_ratio * buf.rew_buf[
slice(buf.path_start_idx + 1,
buf.ptr)] #!add GAIL reward 最後の報酬は含まれないため長さが１短い
if d: # if trajectory didn't reach terminal state, bootstrap value target
last_val = r_env_ratio * r + last_val_gail
else:
last_val = sess.run(v,
feed_dict={x_ph: o.reshape(1, -1)
}) #v_last=...だったけどこれで良さげ
buf.finish_path(
last_val) #これの前にbuf.finish_add_r_vがなされていることを確認すべし
if terminal:
#only store trajectory to SIBUffer if trajectory finished
if trj_full:
Sibuffer.store(
agent_obs, agent_act,
sum_reward=ep_ret_task) #!store trajectory
else:
Sibuffer.store(
agent_obs, agent_act,
sum_reward=ep_ret_task) #!store trajectory
logger.store(EpRet=ep_ret_task,
EpRet_Sum=ep_ret_sum,
EpRet_Gail=ep_ret_gail,
EpLen=ep_len)
o, r, d, ep_ret_task, ep_ret_sum, ep_ret_gail, ep_len = env.reset(
), 0, False, 0, 0, 0, 0
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, epoch)
# Perform PPO update!
if not (trj_full):
M_obs_buf = Sibuffer.get_obs_trj()
trj_full = (M_obs_buf.shape[0] >= buf_size)
if trj_full: #replaybufferがr_thresholdよりも大きいとき
Sibuffer.update_main_buf(ratio_update=si_update_ratio,
update_type=buf_update_type)
M_obs_buf = Sibuffer.get_obs_trj()
M_act_buf = Sibuffer.get_act_trj()
d_batch_size = len(agent_obs)
for _t in range(d_itr):
e_obs_batch, e_act_batch = Sibuffer.get_random_batch(
d_batch_size)
D.train(sess, e_obs_batch, e_act_batch, agent_obs, agent_act)
D_js_m.train(sess, M_obs_buf, M_act_buf, e_obs,
e_act) #バッファとエキスパートの距離を見るためにtrain
js_d = D.get_js_div(sess, Sibuffer.main_obs_buf,
Sibuffer.main_act_buf, agent_obs, agent_act)
js_d_m = D_js_m.get_js_div(sess, M_obs_buf, M_act_buf, e_obs,
e_act)
else:
js_d, js_d_m = 0.5, 0.5
update()
Sibuffer.store_js(js_d)
logger.store(JS=js_d,
JS_M=js_d_m,
JS_Ratio=Sibuffer.js_ratio_with_random)
# Log info about epoch
#if epoch%10 == 0:#logger print each 10 epoch
logger.log_tabular('Epoch', epoch)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpRet_Sum', average_only=True)
logger.log_tabular('EpRet_Gail', 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('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.log_tabular('Std', average_only=True)
logger.log_tabular('buffer_r', Sibuffer.buffer_r_average)
logger.log_tabular('JS', average_only=True)
logger.log_tabular('JS_M', average_only=True)
logger.log_tabular('JS_Ratio', average_only=True)
logger.dump_tabular()
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 mbfq(env_fn,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=2000,
epochs=100,
replay_size=int(1e6),
gamma=0.99,
polyak=0.995,
lr=1e-3,
alpha=0.2,
batch_size=128,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
max_ep_len_ppo=50,
logger_kwargs=dict(),
save_freq=1,
update_factor=1,
device='cuda',
lam=0.97,
steps_per_ppo_update=1000,
n_ppo_updates=1,
train_pi_iters=80,
target_kl=0.01,
clip_ratio=0.2):
device = torch.device(device)
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]
state_dim = {376: 144, 111: 64, 17: 12, 11: 8}[obs_dim[0]]
# Create actor-critic module and target networks
ac = core.MLPActorCritic(env.observation_space, env.action_space,
**ac_kwargs).to(device)
model = core.FlowWorldModel(obs_dim[0], state_dim,
act_dim + int(act_dim % 2)).to(device)
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,
device=device)
ppo_buffer = PPOBuffer(obs_dim,
act_dim,
steps_per_ppo_update,
gamma=gamma,
lam=lam,
device=device)
# 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, model])
logger.log('\nNumber of parameters: \t pi: %d, \t q: %d, \t model: %d\n' %
var_counts)
# Set up function for computing SAC Q-losses
def compute_loss_model(data):
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
if act_dim % 2:
a = torch.cat([a, torch.zeros(len(a), 1, device=a.device)], dim=1)
loss, info, _ = model(o, a, r, o2, d)
return loss, info
# 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().cpu().numpy(),
Q2Vals=q2.detach().cpu().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().cpu().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)
model_optimizer = SparseDenseAdamOptimizer(model,
dense_args={'lr': lr},
sparse_args={'lr': 10 * lr})
# Set up model saving
logger.setup_pytorch_saver(ac)
def update(data):
loss_model, model_info = compute_loss_model(data)
model_optimizer.zero_grad()
loss_model.backward()
core.clip_grad_norm(model.parameters(), 1000)
model_optimizer.step()
# Record things
logger.store(LossModel=loss_model.item(), **model_info)
# 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):
# s = model.get_state(torch.as_tensor(o, dtype=torch.float32, device=device).unsqueeze(0), batch_size=batch_size).squeeze(0)
# return ac.act(o, deterministic)
return ac.act(torch.as_tensor(o, dtype=torch.float32, device=device),
deterministic)
# Set up function for computing PPO policy loss
def ppo_compute_loss_pi(data):
obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data[
'logp']
# Policy loss
ac.pi(obs)
logp = ac.pi.log_prob(act, desquash=True)
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()
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, cf=clipfrac)
return loss_pi, pi_info
def ppo_step(o):
with torch.no_grad():
o = torch.as_tensor(o, dtype=torch.float32, device=device)
a, log_pi = ac_targ.pi(o)
q1_pi = ac.q1(o, a)
q2_pi = ac.q2(o, a)
q_pi = torch.min(q1_pi, q2_pi)
v = (alpha * log_pi - q_pi).squeeze(0).cpu().numpy()
return a.squeeze(0).cpu().numpy(), v, log_pi.squeeze(0).cpu().numpy()
def virtual_ppo():
venv = VirtualEnv(replay_buffer, model)
ac_targ.pi.load_state_dict(ac.pi.state_dict())
# Main loop: collect experience in env and update/log each epoch
for epoch in range(n_ppo_updates):
o, ep_ret, ep_len = venv.reset(), 0, 0
for t in tqdm(range(steps_per_ppo_update)):
a, v, log_pi = ppo_step(o)
next_o, r, d, _ = venv.step(a)
ep_ret += r
ep_len += 1
# save and log
ppo_buffer.store(o, a, r, v, log_pi)
logger.store(VVals=v)
# Update obs (critical!)
o = next_o
timeout = ep_len == max_ep_len_ppo
terminal = d or timeout
epoch_ended = t == 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, _ = ppo_step(o)
else:
v = 0
ppo_buffer.finish_path(v)
if terminal:
# only save EpRet / EpLen if trajectory finished
logger.store(VirtualEpRet=ep_ret, VirtualEpLen=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!
data = ppo_buffer.get()
pi_l_old, pi_info_old = ppo_compute_loss_pi(data)
pi_l_old = pi_l_old.item()
# Train policy with multiple steps of gradient descent
for i in range(train_pi_iters):
loss_pi, pi_info = ppo_compute_loss_pi(data)
# kl = mpi_avg(pi_info['kl'])
kl = pi_info['kl']
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' %
i)
break
pi_optimizer.zero_grad()
loss_pi.backward()
# mpi_avg_grads(ac.pi) # average grads across MPI processes
pi_optimizer.step()
logger.store(StopIter=i)
# Log changes from update
kl, cf = pi_info['kl'], pi_info['cf']
logger.store(LossPi=pi_l_old,
KL=kl,
ClipFrac=cf,
DeltaLossPi=(loss_pi.item() - pi_l_old))
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
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 tqdm(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 * update_factor):
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()
virtual_ppo()
# 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('VirtualEpRet', with_min_and_max=True)
logger.log_tabular('VirtualEpLen', 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', with_min_and_max=True)
logger.log_tabular('LossPi', average_only=True)
logger.log_tabular('LossQ', average_only=True)
logger.log_tabular('LossModel', average_only=True)
logger.log_tabular('reg', average_only=True)
logger.log_tabular('rec', average_only=True)
logger.log_tabular('loss_d', average_only=True)
logger.log_tabular('loss_r', average_only=True)
logger.log_tabular('kl', average_only=True)
logger.log_tabular('prior_logprob', average_only=True)
logger.log_tabular('log_det', average_only=True)
logger.log_tabular('conditional_log_det', average_only=True)
logger.log_tabular('conditional_logprob', 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()
class sac_discrete_class:
def __init__(self,
env_fn,
Actor=core.DiscreteMLPActor,
Critic=core.DiscreteMLPQFunction,
ac_kwargs=dict(),
seed=0,
steps_per_epoch=4000,
epochs=100,
replay_size=int(5e5),
gamma=0.99,
polyak=0.995,
lr=1e-5,
alpha=0.2,
batch_size=100,
start_steps=10000,
update_after=1000,
update_times_every_step=50,
num_test_episodes=10,
max_ep_len=2000,
logger_kwargs=dict(),
save_freq=1,
automatic_entropy_tuning=True,
use_gpu=False,
gpu_parallel=False,
show_test_render=False,
last_save_path=None,
state_of_art_model=False,
**kwargs):
"""
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_times_every_step (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.
"""
self.ac_kwargs = ac_kwargs
self.seed = seed
self.steps_per_epoch = steps_per_epoch
self.epochs = epochs
self.replay_size = replay_size
self.gamma = gamma
self.polyak = polyak
self.lr = lr
self.alpha = alpha
self.batch_size = batch_size
self.start_steps = start_steps
self.update_after = update_after
self.update_times_every_step = update_times_every_step
self.num_test_episodes = num_test_episodes
self.max_ep_len = max_ep_len
self.logger_kwargs = logger_kwargs
self.save_freq = save_freq
self.automatic_entropy_tuning = automatic_entropy_tuning
self.use_gpu = use_gpu
self.gpu_parallel = gpu_parallel
self.show_test_render = show_test_render
self.last_save_path = last_save_path
self.kwargs = kwargs
self.logger = EpochLogger(**logger_kwargs)
self.logger.save_config(locals())
torch.manual_seed(seed)
np.random.seed(seed)
self.env = env_fn()
self.test_env = env_fn()
self.env.seed(seed)
# env.seed(seed)
# test_env.seed(seed)
self.obs_dim = self.env.observation_space.shape
self.act_dim = self.env.action_space.n
# Create actor-critic module and target networks
self.state_of_art_model = state_of_art_model
if self.state_of_art_model:
self.actor = Actor(**ac_kwargs)
self.critic1 = Critic(**ac_kwargs)
self.critic2 = Critic(**ac_kwargs)
self.critic1_targ = deepcopy(self.critic1)
self.critic2_targ = deepcopy(self.critic2)
else:
self.actor = Actor(self.obs_dim, self.act_dim, **ac_kwargs)
self.critic1 = Critic(self.obs_dim, self.act_dim, **ac_kwargs)
self.critic2 = Critic(self.obs_dim, self.act_dim, **ac_kwargs)
self.critic1_targ = deepcopy(self.critic1)
self.critic2_targ = deepcopy(self.critic2)
# gpu是否使用
if torch.cuda.is_available():
self.device = torch.device("cuda" if self.use_gpu else "cpu")
if gpu_parallel:
self.actor = torch.nn.DataParallel(self.actor)
self.critic1 = torch.nn.DataParallel(self.critic1)
self.critic2 = torch.nn.DataParallel(self.critic2)
self.critic1_targ = torch.nn.DataParallel(self.critic1_targ)
self.critic2_targ = torch.nn.DataParallel(self.critic2_targ)
else:
self.use_gpu = False
self.gpu_parallel = False
self.device = torch.device("cpu")
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in self.critic1_targ.parameters():
p.requires_grad = False
for p in self.critic2_targ.parameters():
p.requires_grad = False
self.actor.to(self.device)
self.critic1.to(self.device)
self.critic2.to(self.device)
self.critic1_targ.to(self.device)
self.critic2_targ.to(self.device)
# Experience buffer
self.replay_buffer = ReplayBuffer(obs_dim=self.obs_dim,
act_dim=1,
size=replay_size,
device=self.device)
# # List of parameters for both Q-networks (save this for convenience)
# q_params = itertools.chain(critic1.parameters(), critic2.parameters())
if self.automatic_entropy_tuning:
# we set the max possible entropy as the target entropy
self.target_entropy = -np.log((1.0 / self.act_dim)) * 0.98
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha = self.log_alpha.exp()
self.alpha_optim = Adam([self.log_alpha], lr=lr, eps=1e-4)
# Count variables (protip: try to get a feel for how different size networks behave!)
var_counts = tuple(
core.count_vars(module)
for module in [self.actor, self.critic1, self.critic2])
self.logger.log(
'\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d\n' %
var_counts)
# Set up optimizers for policy and q-function
self.pi_optimizer = Adam(self.actor.parameters(), lr=lr)
self.q1_optimizer = Adam(self.critic1.parameters(), lr=lr)
self.q2_optimizer = Adam(self.critic2.parameters(), lr=lr)
if last_save_path is not None:
checkpoints = torch.load(last_save_path)
self.epoch = checkpoints['epoch']
self.actor.load_state_dict(checkpoints['actor'])
self.critic1.load_state_dict(checkpoints['critic1'])
self.critic2.load_state_dict(checkpoints['critic2'])
self.pi_optimizer.load_state_dict(checkpoints['pi_optimizer'])
self.q1_optimizer.load_state_dict(checkpoints['q1_optimizer'])
self.q2_optimizer.load_state_dict(checkpoints['q2_optimizer'])
self.critic1_targ.load_state_dict(checkpoints['critic1_targ'])
self.critic2_targ.load_state_dict(checkpoints['critic2_targ'])
# last_best_Return_per_local = checkpoints['last_best_Return_per_local']
print("succesfully load last prameters")
else:
self.epoch = 0
print("Dont load last prameters.")
# Set up function for computing SAC Q-losses
def compute_loss_q(self, data):
# Bellman backup for Q functions
with torch.no_grad():
o, a, r, o2, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
r = r.unsqueeze(-1) if r.ndim == 1 else r
d = d.unsqueeze(-1) if d.ndim == 1 else d
if self.state_of_art_model and o.ndim != 4:
o = o.unsqueeze(dim=1)
o2 = o2.unsqueeze(dim=1)
# Target actions come from *current* policy
a2, (a2_p, logp_a2), _ = self.get_action(o2)
# Target Q-values
q1_pi_targ = self.critic1_targ(o2)
q2_pi_targ = self.critic2_targ(o2)
q_pi_targ = torch.min(q1_pi_targ, q2_pi_targ)
min_qf_next_target = a2_p * (q_pi_targ - self.alpha * logp_a2)
min_qf_next_target = min_qf_next_target.mean(dim=1).unsqueeze(-1)
backup = r + self.gamma * (1 - d) * min_qf_next_target
q1 = self.critic1(o).gather(1, a.long())
q2 = self.critic2(o).gather(1, a.long())
# MSE loss against Bellman backup
loss_q1 = F.mse_loss(q1, backup)
loss_q2 = F.mse_loss(q2, backup)
# Useful info for logging
q_info = dict(Q1Vals=q1.detach().cpu().numpy(),
Q2Vals=q2.detach().cpu().numpy())
return loss_q1, loss_q2, q_info
# Set up function for computing SAC pi loss
def compute_loss_pi(self, data):
state_batch = data['obs']
if self.state_of_art_model and state_batch.ndim != 4:
state_batch = state_batch.unsqueeze(dim=1)
action, (action_probabilities,
log_action_probabilities), _ = self.get_action(state_batch)
qf1_pi = self.critic1(state_batch)
qf2_pi = self.critic2(state_batch)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
inside_term = self.alpha * log_action_probabilities - min_qf_pi
policy_loss = action_probabilities * inside_term
policy_loss = policy_loss.mean()
log_action_probabilities = torch.sum(log_action_probabilities *
action_probabilities,
dim=1)
# Useful info for logging
pi_info = dict(LogPi=log_action_probabilities.detach().cpu().numpy())
return policy_loss, log_action_probabilities, pi_info
def take_optimisation_step(self,
optimizer,
network,
loss,
clipping_norm=None,
retain_graph=False):
if not isinstance(network, list):
network = [network]
optimizer.zero_grad() # reset gradients to 0
loss.backward(
retain_graph=retain_graph) # this calculates the gradients
if clipping_norm is not None:
for net in network:
torch.nn.utils.clip_grad_norm_(
net.parameters(),
clipping_norm) # clip gradients to help stabilise training
optimizer.step() # this applies the gradients
def soft_update_of_target_network(self, local_model, target_model, tau):
"""Updates the target network in the direction of the local network but by taking a step size
less than one so the target network's parameter values trail the local networks. This helps stabilise training"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def update(self, data):
# First run one gradient descent step for Q1 and Q2
loss_q1, loss_q2, q_info = self.compute_loss_q(data)
self.take_optimisation_step(
self.q1_optimizer,
self.critic1,
loss_q1,
5,
)
self.take_optimisation_step(
self.q2_optimizer,
self.critic2,
loss_q2,
5,
)
# Record things
self.logger.store(LossQ=(loss_q1.item() + loss_q2.item()) / 2.,
**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.
loss_pi, log_pi, pi_info = self.compute_loss_pi(data)
# Record things
self.logger.store(LossPi=loss_pi.item(), **pi_info)
# # Unfreeze Q-networks so you can optimize it at next DDPG step.
# for p in q_params:
# p.requires_grad = True
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
# logger.store(alpha_loss=alpha_loss.item())
self.take_optimisation_step(
self.pi_optimizer,
self.actor,
loss_pi,
5,
)
with torch.no_grad():
for p, p_targ in zip(self.critic1.parameters(),
self.critic1_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_(self.polyak)
p_targ.data.add_((1 - self.polyak) * p.data)
for p, p_targ in zip(self.critic2.parameters(),
self.critic2_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_(self.polyak)
p_targ.data.add_((1 - self.polyak) * p.data)
if self.automatic_entropy_tuning:
self.take_optimisation_step(self.alpha_optim, None, alpha_loss,
None)
self.alpha = self.log_alpha.exp()
def get_action(self, state):
"""Given the state, produces an action, the probability of the action, the log probability of the action, and
the argmax action"""
action_probabilities = self.actor(state)
max_probability_action = torch.argmax(action_probabilities).unsqueeze(
0)
action_distribution = Categorical(action_probabilities)
action = action_distribution.sample().cpu()
# Have to deal with situation of 0.0 probabilities because we can't do log 0
z = action_probabilities == 0.0
z = z.float() * 1e-8
log_action_probabilities = torch.log(action_probabilities + z)
return action, (action_probabilities,
log_action_probabilities), max_probability_action
def test_agent(self):
for j in range(self.num_test_episodes):
o, d, ep_ret, ep_len = self.test_env.reset(
isRandomStart=True), False, 0, 0
while not (ep_len == self.max_ep_len):
if self.show_test_render:
self.test_env.render()
# Take deterministic actions at test time
with torch.no_grad():
if self.state_of_art_model and o.ndim == 2:
obs = torch.FloatTensor(o).view([1, 1, *self.obs_dim
]).to(self.device)
else:
obs = torch.FloatTensor(o).view([1, *self.obs_dim
]).to(self.device)
_, (_, _), a = self.get_action(obs)
o, r, d, _ = self.test_env.step(a.cpu().item())
ep_ret += r
ep_len += 1
text = "Test: Code: %s, Epoch: %s, TestEp_ret: %s, Testep_len: %s." % \
(self.test_env.current_env.code, self.epoch, ep_ret, ep_len)
self.logger.log_stdout(text)
if d == 1: # 资金不足
print('test资金不足')
break
elif d == 2: # 达到索引终点
print('test达到合约终点, 重新开始')
self.test_env.reset(isRandomStart=True,
total=self.test_env.current_env.total)
elif d == 3: # 达到回撤限制
print('test达到回撤限制')
break
self.logger.store(TestEpRet=ep_ret, TestEpLen=ep_len)
def run(self):
# Prepare for interaction with environment
total_steps = self.steps_per_epoch * self.epochs
start_time = time.time()
o, ep_ret, ep_len = self.env.reset(), 0, 0
eps = 1
t = self.epoch * self.steps_per_epoch if self.last_save_path is not None else 0
# Main loop: collect experience in env and update/log each epoch
self.actor.eval()
while t < total_steps:
text = "Code: %s, Epoch: %s, Episode: %s, Ep_ret: %s, ep_len: %s. [%s/%s]" % \
(self.env.current_env.code, self.epoch, eps, ep_ret, ep_len, t + 1, total_steps)
self.logger.log_stdout(text)
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
if t >= self.start_steps:
with torch.no_grad():
if self.state_of_art_model and o.ndim == 2:
obs = torch.FloatTensor(o).view([1, 1, *self.obs_dim
]).to(self.device)
else:
obs = torch.FloatTensor(o).view([1, *self.obs_dim
]).to(self.device)
a, _, _ = self.get_action(obs)
a = a.cpu().item()
else:
a = np.random.randint(0, self.act_dim)
# Step the env
o2, r, d, _ = self.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 == self.max_ep_len else d # 如果长度==最大长度则False,否则
# Store experience to replay buffer
if d == 2 or d == 1: # 控制重置
done = 1
else:
done = 0
self.replay_buffer.store(o, a, r, o2, done)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
o = o2
# End of trajectory handling
if d == 1 or (ep_len == self.max_ep_len
): # ep_len == max_ep_len是游戏成功时最少ep长度
o, ep_ret, ep_len = self.env.reset(isRandomStart=False), 0, 0
eps += 1
elif d == 2: # 达到索引终点
self.env.reset(
isRandomStart=False,
total=self.env.current_env.total) #继续下一个合约,但是继承上一次总资产
elif d == 3: # 达到回撤限制(目前先不管)
d
# Update handling
if self.replay_buffer.size > self.update_after and t % self.update_times_every_step == 0:
self.actor.train()
for j in range(self.update_times_every_step):
batch = self.replay_buffer.sample_batch(self.batch_size)
self.update(data=batch)
self.actor.eval()
# logger.save_epoch_Ret_optimizer_model(save_dict)
# last_best_Return_per_local = Return_per_local
# End of epoch handling
if (
t + 1
) % self.steps_per_epoch == 0 and self.replay_buffer.size > self.update_after:
if (
t + 1
) % self.update_times_every_step == 0: # 每达到update_times_every_step
self.epoch = (t + 1) // self.steps_per_epoch
# Save model
if proc_id() == 0 and (self.epoch) % self.save_freq == 0:
save_dict = {
'epoch': self.epoch,
'actor': self.actor.state_dict(),
'critic1': self.critic1.state_dict(),
'critic2': self.critic2.state_dict(),
'pi_optimizer': self.pi_optimizer.state_dict(),
'q1_optimizer': self.q1_optimizer.state_dict(),
'q2_optimizer': self.q2_optimizer.state_dict(),
'critic1_targ': self.critic1_targ.state_dict(),
'critic2_targ': self.critic2_targ.state_dict(),
}
self.logger.save_epoch_Ret_optimizer_model(save_dict)
self.actor.eval()
# Test the performance of the deterministic version of the agent.
self.test_agent()
# Log info about epoch
self.logger.log_tabular('Epoch', self.epoch)
# self.logger.log_tabular('EpRet', with_min_and_max=True)
self.logger.log_tabular('TestEpRet',
with_min_and_max=False)
# self.logger.log_tabular('EpLen', average_only=True)
self.logger.log_tabular('TestEpLen', average_only=True)
self.logger.log_tabular('TotalEnvInteracts', t)
self.logger.log_tabular('Q1Vals', with_min_and_max=True)
self.logger.log_tabular('Q2Vals', with_min_and_max=True)
self.logger.log_tabular('LogPi', with_min_and_max=True)
self.logger.log_tabular('LossPi', average_only=True)
self.logger.log_tabular('LossQ', average_only=True)
self.logger.log_tabular('Time', time.time() - start_time)
# if epoch > 1:
# (time.time() - start_time)/epo
self.logger.dump_tabular()
t += 1
def a2c(env_fn,
agent: Agent,
seed=0,
num_cpu=1,
device=torch.device("cpu"),
epochs=1000,
steps_per_epoch=100,
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,
grid_layer_weight_reg_loss_coef=1e-4,
save_every=100,
log_every=10,
logger_kwargs=dict(),
test_every=100,
num_test_episodes=5,
deterministic=False,
save_freq=1,
solved_score=None,
render=False,
):
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['agent']
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()
assert env.max_episode_steps > 0
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
# training model and target model
target_agent = deepcopy(agent)
if use_MPI:
# Sync params across processes
mpi_pytorch.sync_params(agent)
mpi_pytorch.sync_params(target_agent)
# Freeze target networks with respect to optimizers (only update via polyak averaging)
for p in target_agent.parameters():
p.requires_grad = False
# Utilize GPU
agent.to(device)
target_agent.to(device)
# Set up optimizers for policy and q-function
optimizer = Adam(agent.parameters(), lr=learning_rate)
# Set up model saving
logger.setup_pytorch_saver(agent, name='model')
def update(episode_buffer):
# Update
if episode_buffer.dones[-1]:
next_value = 0.0
else:
last_obs = episode_buffer.next_observations[-1]
previous_reward = episode_buffer.rewards[-1]
last_obs_tensor = torch.tensor(last_obs, dtype=torch.float32).unsqueeze(0)
previous_reward_tensor = torch.tensor([previous_reward], dtype=torch.float32).unsqueeze(0)
context = agent.get_context()
next_value = target_agent.predict_value(obs_tensor=last_obs_tensor,
previous_reward_tensor=previous_reward_tensor,
goal_grid_code_tensor=goal_grid_code_tensor,
context=context).cpu().item()
# Super critical!!
optimizer.zero_grad()
# Compute value and policy losses
loss, info = agent.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,
grid_layer_wreg_loss_coef=grid_layer_weight_reg_loss_coef)
loss.backward()
if use_MPI:
mpi_pytorch.mpi_avg_grads(agent)
# Optimize
if max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(agent.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(agent.parameters(), target_agent.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_agent)
# 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()
reward = 0
goal_grid_code_tensor = None
# Reset episode stats
episode_return = 0
episode_length = 0
for epoch in range(1, epochs + 1):
agent.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)
previous_reward_tensor = torch.tensor([reward], dtype=torch.float32).unsqueeze(0)
action = agent.step(obs_tensor, previous_reward_tensor, goal_grid_code_tensor).squeeze(0)
# Step the env
obs2, reward, done, _ = env.step(action.detach().cpu().item())
if render and mpi_tools.proc_id() == 0:
env.render('human', view='top')
time.sleep(1e-3)
episode_return += reward
episode_length += 1
# Store transition to history
epoch_history.store(observation=None, action=None, 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:
if reward > 0:
goal_grid_code_tensor = agent.current_grid_code.detach()
break
update(epoch_history)
# if done
if epoch_history.dones[-1]:
logger.store(EpRet=episode_return, EpLen=episode_length)
# Reset env
obs = env.reset()
agent.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('LossGridL2', average_only=True)
logger.log_tabular('LossPIM', 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:
video_dir = pathlib.Path(logger.output_dir) / 'test_videos' / f'epoch-{epoch:d}'
test_env_fn = lambda: Monitor(env_fn(), directory=video_dir)
# Test the performance of the deterministic version of the agent.
context = agent.get_context()
agent.eval()
episode_info = evaluate_agent(env_fn=test_env_fn,
agent=agent,
deterministic=deterministic,
num_episodes=num_test_episodes,
render=False,
logger=test_logger)
agent.train()
agent.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
torch.save(agent, str(logger.output_dir / 'agent.pt'))
env.close()
def vpg(env_fn, actor_critic=core.ActorCritic, 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):
"""
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 an input ``x``, and action, ``a``, and returns:
=========== ================ ======================================
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``
| in states ``x``.
``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``. (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()
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 = 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
# Experience buffer
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = VPGBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam)
# Actor Critic model instance
actor_critic = actor_critic(obs_dim, **ac_kwargs)
actor_critic.to(device) # load to cpu/gpu
# Count variables
var_counts = tuple(core.count_vars(model) for model in [actor_critic.policy, actor_critic.value])
logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n'%var_counts)
# Optimizers
train_pi = optim.Adam(actor_critic.policy.parameters(), lr=pi_lr)
train_v = optim.Adam(actor_critic.value.parameters(), lr=vf_lr)
# Sync params across processes
# sync_all_params() # TODO figure out the way to do use MPI for pytorch
def update():
actor_critic.train()
obs, act, adv, ret, logp_old = map(lambda x: Tensor(x).to(device), buf.get())
_ , logp, _, val = actor_critic(obs, act)
ent = (-logp).mean()
# VPG objectives
pi_loss = -(logp * adv).mean()
v_l_old = ((ret - val)**2).mean()
# Policy gradient step
train_pi.zero_grad()
pi_loss.backward()
train_pi.step()
# Value function learning
for _ in range(train_v_iters):
val = actor_critic.value(obs)
v_loss = (ret - val).pow(2).mean()
train_v.zero_grad()
v_loss.backward()
train_v.step()
actor_critic.eval()
# Log changes from update
_, logp, _, val = actor_critic(obs, act)
pi_l_new = -(logp * adv).mean()
v_l_new = ((ret - val)**2).mean()
kl = (logp_old - logp).mean()
logger.store(LossPi=pi_loss, LossV=v_l_old,
KL=kl, Entropy=ent,
DeltaLossPi=(pi_l_new - pi_loss),
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, logp_t, logp_pi_t, v_t = actor_critic(Tensor(o.reshape(1,-1)).to(device))
# save and log
buf.store(o, a.cpu().numpy(), r, v_t.item(), logp_pi_t.cpu().detach().numpy())
logger.store(VVals=v_t)
o, r, d, _ = env.step(a.cpu().numpy())
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 actor_critic(Tensor(o.reshape(1,-1)).to(device))[-1].item()
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}, actor_critic, None)
# Perform VPG 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('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=256,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1,
device='cuda',
override=True):
"""
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.
"""
device = torch.device(device)
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)
# 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,
device=device)
# 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().cpu().numpy(),
Q2Vals=q2.detach().cpu().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().cpu().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, device=device),
deterministic)
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
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 tqdm(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, 'rb': replay_buffer.get_state()}, None)
logger.save_state({'env': env}, None if override else epoch)
# 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('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 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
maxRev = float("-inf") #negative infinity in the beginning
#maxRevActionSeq=[]
maxRevTSTT = 0
maxRevRevenue = 0
maxRevThroughput = 0
maxRevJAH = 0
maxRevRemVeh = 0
maxRevJAH2 = 0
maxRevRMSE_MLvio = 0
maxRevPerTimeVio = 0
maxRevHOTDensity = pd.DataFrame()
maxRevGPDensity = pd.DataFrame()
maxtdJAHMax = 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)
#we need to scale the sampled values of action from (-1,1) to our choices of toll coz they were sampled from tanh activation mu
numpyFromA = np.array(a[0])
numpyFromA = ((numpyFromA + 1.0) *
(env.state.tollMax - env.state.tollMin) /
2.0) + env.state.tollMin
a[0] = np.ndarray.tolist(numpyFromA)
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)
#get other stats and store them too
otherStats = env.getAllOtherStats()
if np.any(np.isnan(np.array(otherStats))):
sys.exit("Nan found in statistics! Error")
logger.store(EpTSTT=otherStats[0],
EpRevenue=otherStats[1],
EpThroughput=otherStats[2],
EpJAH=otherStats[3],
EpRemVeh=otherStats[4],
EpJAH2=otherStats[5],
EpMLViolRMSE=otherStats[6],
EpPerTimeVio=otherStats[7],
EptdJAHMax=otherStats[8])
#determine max rev profile
if ep_ret > maxRev:
maxRev = ep_ret
maxRevActionSeq = env.state.tollProfile
maxRevTSTT = otherStats[0]
maxRevRevenue = otherStats[1]
maxRevThroughput = otherStats[2]
maxRevJAH = otherStats[3]
maxRevRemVeh = otherStats[4]
maxRevJAH2 = otherStats[5]
maxRevRMSE_MLvio = otherStats[6]
maxRevPerTimeVio = otherStats[7]
maxRevHOTDensity = env.getHOTDensityData()
maxRevGPDensity = env.getGPDensityData()
maxtdJAHMax = otherStats[8]
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('EpTSTT', average_only=True)
logger.log_tabular('EpRevenue', average_only=True)
logger.log_tabular('EpThroughput', average_only=True)
logger.log_tabular('EpJAH', average_only=True)
logger.log_tabular('EpRemVeh', average_only=True)
logger.log_tabular('EpJAH2', average_only=True)
logger.log_tabular('EpMLViolRMSE', average_only=True)
logger.log_tabular('EpPerTimeVio', average_only=True)
logger.log_tabular('EptdJAHMax', 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('ClipFrac', average_only=True)
logger.log_tabular('StopIter', average_only=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
print("Max cumulative reward obtained= %f " % maxRev)
print(
"Corresponding revenue($)= %f, TSTT(hrs)= %f, Throughput(veh)=%f, JAHstat= %f, remaining vehicles= %f, JAHstat2=%f, RMSEML_vio=%f, percentTimeViolated(%%)=%f, tdJAHMax= %f"
%
(maxRevRevenue, maxRevTSTT, maxRevThroughput, maxRevJAH, maxRevRemVeh,
maxRevJAH2, maxRevRMSE_MLvio, maxRevPerTimeVio, maxtdJAHMax))
outputVector = [
maxRev, maxRevRevenue, maxRevTSTT, maxRevThroughput, maxRevJAH,
maxRevRemVeh, maxRevJAH2, maxRevRMSE_MLvio, maxRevPerTimeVio,
maxtdJAHMax
]
#print("\n===Max rev action sequence is\n",maxRevActionSeq)
exportTollProfile(maxRevActionSeq, logger_kwargs, outputVector)
exportDensityData(maxRevHOTDensity, maxRevGPDensity, logger_kwargs)
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
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])
'''
# 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 egl(env_fn,
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=256,
start_steps=10000,
update_after=1000,
update_every=50,
num_test_episodes=10,
max_ep_len=1000,
logger_kwargs=dict(),
save_freq=1,
eps=0.4,
n_explore=32,
device='cuda',
architecture='mlp',
sample='on_policy'):
"""
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.
"""
if architecture == 'mlp':
actor_critic = core.MLPActorCritic
elif architecture == 'spline':
actor_critic = core.SplineActorCritic
else:
raise NotImplementedError
device = torch.device(device)
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)
# 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,
device=device)
# 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, ac.geps])
logger.log(
'\nNumber of parameters: \t pi: %d, \t q1: %d, \t q2: %d, \t geps: %d\n'
% var_counts)
n_samples = 100
cmin = 0.25
cmax = 1.75
greed = 0.01
rand = 0.01
def max_reroute(o):
b, _ = o.shape
o = repeat_and_reshape(o, n_samples)
with torch.no_grad():
ai, _ = ac.pi(o)
q1 = ac.q1(o, ai)
q2 = ac.q2(o, ai)
qi = torch.min(q1, q2).unsqueeze(-1)
qi = qi.view(n_samples, b, 1)
ai = ai.view(n_samples, b, act_dim)
rank = torch.argsort(torch.argsort(qi, dim=0, descending=True),
dim=0,
descending=False)
w = cmin * torch.ones_like(ai)
m = int((1 - cmin) * n_samples / (cmax - cmin))
w += (cmax - cmin) * (rank < m).float()
w += ((1 - cmin) * n_samples - m * (cmax - cmin)) * (rank == m).float()
w -= greed
w += greed * n_samples * (rank == 0).float()
w = w * (1 - rand) + rand
w = w / w.sum(dim=0, keepdim=True)
prob = torch.distributions.Categorical(probs=w.permute(1, 2, 0))
a = torch.gather(ai.permute(1, 2, 0), 2,
prob.sample().unsqueeze(2)).squeeze(2)
return a, (ai, w.mean(-1))
# 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().cpu().numpy(),
Q2Vals=q2.detach().cpu().numpy())
return loss_q, q_info
# # Set up function for computing EGL mean-gradient-losses
# def compute_loss_g(data):
#
# o, a1, r, o_tag, d = data['obs'], data['act'], data['rew'], data['obs2'], data['done']
#
# a2 = ball_explore(a1, n_explore, eps)
#
# a2 = a2.view(n_explore * len(r), act_dim)
# o_expand = repeat_and_reshape(o, n_explore)
#
# # Bellman backup for Q functions
# with torch.no_grad():
#
# q1 = ac.q1(o_expand, a2)
# q2 = ac.q2(o_expand, a2)
# q_dither = torch.min(q1, q2)
#
# # Target actions come from *current* policy
# a_tag, logp_a_tag = ac.pi(o_tag)
#
# # Target Q-values
# q1_pi_targ = ac_targ.q1(o_tag, a_tag)
# q2_pi_targ = ac_targ.q2(o_tag, a_tag)
# q_pi_targ = torch.min(q1_pi_targ, q2_pi_targ)
# q_anchor = r + gamma * (1 - d) * (q_pi_targ - alpha * logp_a_tag)
#
# q_anchor = repeat_and_reshape(q_anchor, n_explore).squeeze(-1)
#
# geps = ac.geps(o, a1)
# geps = repeat_and_reshape(geps, n_explore)
# a1 = repeat_and_reshape(a1, n_explore)
#
# geps = (geps * (a2 - a1)).sum(-1)
# # l1 loss against Bellman backup
#
# loss_g = F.smooth_l1_loss(geps, q_dither - q_anchor)
#
# # Useful info for logging
# g_info = dict(GVals=geps.flatten().detach().cpu().numpy())
#
# return loss_g, g_info
# Set up function for computing EGL mean-gradient-losses
def compute_loss_g(data):
o, a1, r, o_tag, d = data['obs'], data['act'], data['rew'], data[
'obs2'], data['done']
a2 = ball_explore(a1, n_explore, eps)
a2 = a2.view(n_explore * len(r), act_dim)
o_expand = repeat_and_reshape(o, n_explore)
# Bellman backup for Q functions
with torch.no_grad():
q1 = ac.q1(o_expand, a2)
q2 = ac.q2(o_expand, a2)
q_dither = torch.min(q1, q2)
# Target actions come from *current* policy
# Target Q-values
q1 = ac.q1(o, a1)
q2 = ac.q2(o, a1)
q_anchor = torch.min(q1, q2)
q_anchor = repeat_and_reshape(q_anchor, n_explore).squeeze(-1)
geps = ac.geps(o, a1)
geps = repeat_and_reshape(geps, n_explore)
a1 = repeat_and_reshape(a1, n_explore)
geps = (geps * (a2 - a1)).sum(-1)
# l1 loss against Bellman backup
loss_g = F.smooth_l1_loss(geps, q_dither - q_anchor)
# Useful info for logging
g_info = dict(GVals=geps.flatten().detach().cpu().numpy())
return loss_g, g_info
# Set up function for computing SAC pi loss
def compute_loss_pi(data):
o = data['obs']
pi, logp_pi = ac.pi(o)
geps_pi = ac.geps(o, pi)
# Entropy-regularized policy loss
loss_pi = (alpha * logp_pi - (geps_pi * pi).sum(-1)).mean()
beta = autograd.Variable(pi.detach().clone(), requires_grad=True)
q1_pi = ac.q1(o, beta)
q2_pi = ac.q2(o, beta)
qa = torch.min(q1_pi, q2_pi).unsqueeze(-1)
grad_q = autograd.grad(outputs=qa,
inputs=beta,
grad_outputs=torch.cuda.FloatTensor(
qa.size()).fill_(1.),
create_graph=False,
retain_graph=False,
only_inputs=True)[0]
# Useful info for logging
pi_info = dict(
LogPi=logp_pi.detach().cpu().numpy(),
GradGAmp=torch.norm(geps_pi, dim=-1).detach().cpu().numpy(),
GradQAmp=torch.norm(grad_q, dim=-1).detach().cpu().numpy(),
GradDelta=torch.norm(geps_pi - grad_q,
dim=-1).detach().cpu().numpy(),
GradSim=F.cosine_similarity(geps_pi, grad_q,
dim=-1).detach().cpu().numpy(),
)
return loss_pi, pi_info
if architecture == 'mlp':
# Set up optimizers for policy and q-function
pi_optimizer = Adam(ac.pi.parameters(), lr=lr)
q_optimizer = Adam(q_params, lr=lr)
g_optimizer = Adam(ac.geps.parameters(), lr=lr)
elif architecture == 'spline':
# Set up optimizers for policy and q-function
pi_optimizer = SparseDenseAdamOptimizer(ac.pi,
dense_args={'lr': lr},
sparse_args={'lr': 10 * lr})
q_optimizer = SparseDenseAdamOptimizer([ac.q1, ac.q2],
dense_args={'lr': lr},
sparse_args={'lr': 10 * lr})
g_optimizer = SparseDenseAdamOptimizer(ac.geps,
dense_args={'lr': lr},
sparse_args={'lr': 10 * lr})
else:
raise NotImplementedError
# 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)
# Next run one gradient descent step for the mean-gradient
g_optimizer.zero_grad()
loss_g, g_info = compute_loss_g(data)
loss_g.backward()
g_optimizer.step()
# Record things
logger.store(LossG=loss_g.item(), **g_info)
# Freeze Q-networks so you don't waste computational effort
# computing gradients for them during the policy learning step.
for p in ac.geps.parameters():
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 ac.geps.parameters():
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_on_policy(o, deterministic=False):
return ac.act(torch.as_tensor(o, dtype=torch.float32, device=device),
deterministic)
def get_action_rbi(o, deterministic=False):
o = torch.as_tensor(o, dtype=torch.float32, device=device)
if deterministic:
a = ac.act(o, deterministic)
else:
o = o.unsqueeze(0)
a, _ = max_reroute(o)
a = a.flatten().cpu().numpy()
return a
if sample == 'on_policy':
get_action = get_action_on_policy
elif sample == 'rbi':
get_action = get_action_rbi
else:
raise NotImplementedError
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
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 tqdm(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)
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('GVals', with_min_and_max=True)
logger.log_tabular('LossG', with_min_and_max=True)
logger.log_tabular('GradGAmp', with_min_and_max=True)
logger.log_tabular('GradQAmp', with_min_and_max=True)
logger.log_tabular('GradDelta', with_min_and_max=True)
logger.log_tabular('GradSim', with_min_and_max=True)
logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
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,
explorer=None,
eps=.03,
pretrain_epochs=0):
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
total_epochs = epochs + pretrain_epochs
# Main loop: collect experience in env and update/log each epoch
for epoch in range(total_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)})
# explore if you are in a pretrain epoch or if eps-greedy
pre = epoch < pretrain_epochs
during = random.random() < eps
if pre or during:
if explorer is None:
raise ValueError('Trying to explore but explorer is None')
state = env.env.state_vector()
a = explorer.sample_action(state)
# 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 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 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 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()
# 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()
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))
# 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)
# 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(env,
hidden_sizes,
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):
"""
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.
"""
# Special function to avoid certain slowdowns from PyTorch + MPI combo.
setup_pytorch_for_mpi()
# logger
logger = EpochLogger(**logger_kwargs)
logger.save_config(locals())
# random seeds
seed += 1000 * proc_id()
torch.manual_seed(seed)
np.random.seed(seed)
# 环境
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape
# 创建模型
ac = core.MLPActorCritic(env.observation_space, env.action_space,
hidden_sizes)
# 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. 如果有多个线程，每个线程的经验池长度为 local_steps_per_epoch
local_steps_per_epoch = int(steps_per_epoch / num_procs())
buf = VPGBuffer(obs_dim,
act_dim,
size=local_steps_per_epoch,
gamma=gamma,
lam=lam)
# optimizer
pi_optimizer = torch.optim.Adam(ac.pi.parameters(), lr=pi_lr)
vf_optimizer = torch.optim.Adam(ac.v.parameters(), lr=vf_lr)
# setup model saving
# logger.setup_pytorch_for_mpi()
# interaction
start_time = time.time()
o, ep_ret, ep_len = env.reset(), 0, 0
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)) # (act_dim,), (), ()
next_o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
# save
buf.store(o, a, r, v, logp)
logger.store(VVals=v)
# update obs
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: # timeout=True, terminal=True, epoch_ended=True/False
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:
logger.store(EpRet=ep_ret, EpLen=ep_len)
o, ep_ret, ep_len = env.reset(), 0, 0 # 重新初始化
# Save model
if (epoch % save_freq == 0) or (epoch == epochs - 1):
logger.save_state({'env': env}, None)
# Perform VPG update!
update(buf, ac, train_v_iters, pi_optimizer, vf_optimizer, logger)
# # 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()
