import collections
import itertools
from logging import getLogger
import random
import chainer
import chainer.functions as F
import numpy as np
import chainerrl
from chainerrl import agent
from chainerrl.agents.ppo import _compute_explained_variance
from chainerrl.agents.ppo import _make_dataset
from chainerrl.agents.ppo import _make_dataset_recurrent
from chainerrl.agents.ppo import _yield_subset_of_sequences_with_fixed_number_of_items # NOQA
from chainerrl.misc.batch_states import batch_states
def _get_ordered_params(link):
"""Get a list of parameters sorted by parameter names."""
name_param_pairs = list(link.namedparams())
ordered_name_param_pairs = sorted(name_param_pairs, key=lambda x: x[0])
return [x[1] for x in ordered_name_param_pairs]
def _flatten_and_concat_variables(vs):
"""Flatten and concat variables to make a single flat vector variable."""
return F.concat([F.flatten(v) for v in vs], axis=0)
def _as_ndarray(x):
"""chainer.Variable or ndarray -> ndarray."""
if isinstance(x, chainer.Variable):
return x.array
else:
return x
def _flatten_and_concat_ndarrays(vs):
"""Flatten and concat variables to make a single flat vector ndarray."""
xp = chainer.cuda.get_array_module(vs[0])
return xp.concatenate([_as_ndarray(v).ravel() for v in vs], axis=0)
def _split_and_reshape_to_ndarrays(flat_v, sizes, shapes):
"""Split and reshape a single flat vector to make a list of ndarrays."""
xp = chainer.cuda.get_array_module(flat_v)
sections = np.cumsum(sizes)
vs = xp.split(flat_v, sections)
return [v.reshape(shape) for v, shape in zip(vs, shapes)]
def _replace_params_data(params, new_params_data):
"""Replace data of params with new data."""
for param, new_param_data in zip(params, new_params_data):
assert param.shape == new_param_data.shape
param.array[:] = new_param_data
def _hessian_vector_product(flat_grads, params, vec):
"""Compute hessian vector product efficiently by backprop."""
grads = chainer.grad([F.sum(flat_grads * vec)], params)
assert all(grad is not None for grad in grads),\
"The Hessian-vector product contains None."
grads_data = [grad.array for grad in grads]
return _flatten_and_concat_ndarrays(grads_data)
def _mean_or_nan(xs):
"""Return its mean a non-empty sequence, numpy.nan for a empty one."""
return np.mean(xs) if xs else np.nan
def _find_old_style_function(outputs):
"""Find old-style functions in the computational graph."""
found = []
for v in outputs:
assert isinstance(v, (chainer.Variable, chainer.variable.VariableNode))
if v.creator is None:
continue
if isinstance(v.creator, chainer.Function):
found.append(v.creator)
else:
assert isinstance(v.creator, chainer.FunctionNode)
found.extend(_find_old_style_function(v.creator.inputs))
return found
[docs]class TRPO(agent.AttributeSavingMixin, agent.Agent):
"""Trust Region Policy Optimization.
A given stochastic policy is optimized by the TRPO algorithm. A given
value function is also trained to predict by the TD(lambda) algorithm and
used for Generalized Advantage Estimation (GAE).
Since the policy is optimized via the conjugate gradient method and line
search while the value function is optimized via SGD, these two models
should be separate.
Since TRPO requires second-order derivatives to compute Hessian-vector
products, Chainer v3.0.0 or newer is required. In addition, your policy
must contain only functions that support second-order derivatives.
See https://arxiv.org/abs/1502.05477 for TRPO.
See https://arxiv.org/abs/1506.02438 for GAE.
Args:
policy (Policy): Stochastic policy. Its forward computation must
contain only functions that support second-order derivatives.
Recurrent models are not supported.
vf (ValueFunction): Value function. Recurrent models are not supported.
vf_optimizer (chainer.Optimizer): Optimizer for the value function.
obs_normalizer (chainerrl.links.EmpiricalNormalization or None):
If set to chainerrl.links.EmpiricalNormalization, it is used to
normalize observations based on the empirical mean and standard
deviation of observations. These statistics are updated after
computing advantages and target values and before updating the
policy and the value function.
gamma (float): Discount factor [0, 1]
lambd (float): Lambda-return factor [0, 1]
phi (callable): Feature extractor function
entropy_coef (float): Weight coefficient for entropoy bonus [0, inf)
update_interval (int): Interval steps of TRPO iterations. Every after
this amount of steps, this agent updates the policy and the value
function using data from these steps.
vf_epochs (int): Number of epochs for which the value function is
trained on each TRPO iteration.
vf_batch_size (int): Batch size of SGD for the value function.
standardize_advantages (bool): Use standardized advantages on updates
line_search_max_backtrack (int): Maximum number of backtracking in line
search to tune step sizes of policy updates.
conjugate_gradient_max_iter (int): Maximum number of iterations in
the conjugate gradient method.
conjugate_gradient_damping (float): Damping factor used in the
conjugate gradient method.
act_deterministically (bool): If set to True, choose most probable
actions in the act method instead of sampling from distributions.
value_stats_window (int): Window size used to compute statistics
of value predictions.
entropy_stats_window (int): Window size used to compute statistics
of entropy of action distributions.
kl_stats_window (int): Window size used to compute statistics
of KL divergence between old and new policies.
policy_step_size_stats_window (int): Window size used to compute
statistics of step sizes of policy updates.
Statistics:
average_value: Average of value predictions on non-terminal states.
It's updated before the value function is updated.
average_entropy: Average of entropy of action distributions on
non-terminal states. It's updated on act_and_train.
average_kl: Average of KL divergence between old and new policies.
It's updated after the policy is updated.
average_policy_step_size: Average of step sizes of policy updates
It's updated after the policy is updated.
"""
saved_attributes = ['policy', 'vf', 'vf_optimizer', 'obs_normalizer']
def __init__(self,
policy,
vf,
vf_optimizer,
obs_normalizer=None,
gamma=0.99,
lambd=0.95,
phi=lambda x: x,
entropy_coef=0.01,
update_interval=2048,
max_kl=0.01,
vf_epochs=3,
vf_batch_size=64,
standardize_advantages=True,
batch_states=batch_states,
recurrent=False,
max_recurrent_sequence_len=None,
line_search_max_backtrack=10,
conjugate_gradient_max_iter=10,
conjugate_gradient_damping=1e-2,
act_deterministically=False,
value_stats_window=1000,
entropy_stats_window=1000,
kl_stats_window=100,
policy_step_size_stats_window=100,
logger=getLogger(__name__),
):
self.policy = policy
self.vf = vf
assert policy.xp is vf.xp, 'policy and vf must be on the same device'
if recurrent:
self.model = chainerrl.links.StatelessRecurrentBranched(policy, vf)
else:
self.model = chainerrl.links.Branched(policy, vf)
if policy.xp is not np:
if hasattr(policy, 'device'):
# Link.device is available only from chainer v6
self.model.to_device(policy.device)
else:
self.model.to_gpu(device=policy._device_id)
self.vf_optimizer = vf_optimizer
self.obs_normalizer = obs_normalizer
self.gamma = gamma
self.lambd = lambd
self.phi = phi
self.entropy_coef = entropy_coef
self.update_interval = update_interval
self.max_kl = max_kl
self.vf_epochs = vf_epochs
self.vf_batch_size = vf_batch_size
self.standardize_advantages = standardize_advantages
self.batch_states = batch_states
self.recurrent = recurrent
self.max_recurrent_sequence_len = max_recurrent_sequence_len
self.line_search_max_backtrack = line_search_max_backtrack
self.conjugate_gradient_max_iter = conjugate_gradient_max_iter
self.conjugate_gradient_damping = conjugate_gradient_damping
self.act_deterministically = act_deterministically
self.logger = logger
self.value_record = collections.deque(maxlen=value_stats_window)
self.entropy_record = collections.deque(maxlen=entropy_stats_window)
self.kl_record = collections.deque(maxlen=kl_stats_window)
self.policy_step_size_record = collections.deque(
maxlen=policy_step_size_stats_window)
self.explained_variance = np.nan
assert self.policy.xp is self.vf.xp,\
'policy and vf should be in the same device.'
if self.obs_normalizer is not None:
assert self.policy.xp is self.obs_normalizer.xp,\
'policy and obs_normalizer should be in the same device.'
self.xp = self.policy.xp
self.last_state = None
self.last_action = None
# Contains episodes used for next update iteration
self.memory = []
# Contains transitions of the last episode not moved to self.memory yet
self.last_episode = []
# Batch versions of last_episode, last_state, and last_action
self.batch_last_episode = None
self.batch_last_state = None
self.batch_last_action = None
# Recurrent states of the model
self.train_recurrent_states = None
self.train_prev_recurrent_states = None
self.test_recurrent_states = None
def _initialize_batch_variables(self, num_envs):
self.batch_last_episode = [[] for _ in range(num_envs)]
self.batch_last_state = [None] * num_envs
self.batch_last_action = [None] * num_envs
def _update_if_dataset_is_ready(self):
dataset_size = (
sum(len(episode) for episode in self.memory)
+ len(self.last_episode)
+ (0 if self.batch_last_episode is None else sum(
len(episode) for episode in self.batch_last_episode)))
if dataset_size >= self.update_interval:
self._flush_last_episode()
if self.recurrent:
dataset = _make_dataset_recurrent(
episodes=self.memory,
model=self.model,
phi=self.phi,
batch_states=self.batch_states,
obs_normalizer=self.obs_normalizer,
gamma=self.gamma,
lambd=self.lambd,
max_recurrent_sequence_len=self.max_recurrent_sequence_len,
)
self._update_recurrent(dataset)
else:
dataset = _make_dataset(
episodes=self.memory,
model=self.model,
phi=self.phi,
batch_states=self.batch_states,
obs_normalizer=self.obs_normalizer,
gamma=self.gamma,
lambd=self.lambd,
)
assert len(dataset) == dataset_size
self._update(dataset)
self.explained_variance = _compute_explained_variance(
list(itertools.chain.from_iterable(self.memory)))
self.memory = []
def _flush_last_episode(self):
if self.last_episode:
self.memory.append(self.last_episode)
self.last_episode = []
if self.batch_last_episode:
for i, episode in enumerate(self.batch_last_episode):
if episode:
self.memory.append(episode)
self.batch_last_episode[i] = []
def _update(self, dataset):
"""Update both the policy and the value function."""
if self.obs_normalizer:
self._update_obs_normalizer(dataset)
self._update_policy(dataset)
self._update_vf(dataset)
def _update_recurrent(self, dataset):
"""Update both the policy and the value function."""
flat_dataset = list(itertools.chain.from_iterable(dataset))
if self.obs_normalizer:
self._update_obs_normalizer(flat_dataset)
self._update_policy_recurrent(dataset)
self._update_vf_recurrent(dataset)
def _update_vf_recurrent(self, dataset):
for epoch in range(self.vf_epochs):
random.shuffle(dataset)
for minibatch in _yield_subset_of_sequences_with_fixed_number_of_items( # NOQA
dataset, self.vf_batch_size):
self._update_vf_once_recurrent(minibatch)
def _update_vf_once_recurrent(self, episodes):
xp = self.model.xp
flat_transitions = list(itertools.chain.from_iterable(episodes))
# Prepare data for a recurrent model
seqs_states = []
for ep in episodes:
states = self.batch_states(
[transition['state'] for transition in ep], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
seqs_states.append(states)
flat_vs_teacher = xp.array(
[[transition['v_teacher']] for transition in flat_transitions],
dtype=np.float32)
with chainer.using_config('train', False),\
chainer.no_backprop_mode():
vf_rs = self.vf.concatenate_recurrent_states(
[ep[0]['recurrent_state'][1] for ep in episodes])
flat_vs_pred, _ = self.vf.n_step_forward(
seqs_states, recurrent_state=vf_rs, output_mode='concat')
vf_loss = F.mean_squared_error(flat_vs_pred, flat_vs_teacher)
self.vf_optimizer.update(lambda: vf_loss)
def _update_obs_normalizer(self, dataset):
assert self.obs_normalizer
states = batch_states(
[b['state'] for b in dataset], self.obs_normalizer.xp, self.phi)
self.obs_normalizer.experience(states)
def _update_vf(self, dataset):
"""Update the value function using a given dataset.
The value function is updated via SGD to minimize TD(lambda) errors.
"""
xp = self.vf.xp
assert 'state' in dataset[0]
assert 'v_teacher' in dataset[0]
dataset_iter = chainer.iterators.SerialIterator(
dataset, self.vf_batch_size)
while dataset_iter.epoch < self.vf_epochs:
batch = dataset_iter.__next__()
states = batch_states([b['state'] for b in batch], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
vs_teacher = xp.array(
[b['v_teacher'] for b in batch], dtype=xp.float32)
vs_pred = self.vf(states)
vf_loss = F.mean_squared_error(vs_pred, vs_teacher[..., None])
self.vf_optimizer.update(lambda: vf_loss)
def _compute_gain(self, log_prob, log_prob_old, entropy, advs):
"""Compute a gain to maximize."""
prob_ratio = F.exp(log_prob - log_prob_old)
mean_entropy = F.mean(entropy)
surrogate_gain = F.mean(prob_ratio * advs)
return surrogate_gain + self.entropy_coef * mean_entropy
def _update_policy(self, dataset):
"""Update the policy using a given dataset.
The policy is updated via CG and line search.
"""
assert 'state' in dataset[0]
assert 'action' in dataset[0]
assert 'adv' in dataset[0]
# Use full-batch
xp = self.policy.xp
states = batch_states([b['state'] for b in dataset], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
actions = xp.array([b['action'] for b in dataset])
advs = xp.array([b['adv'] for b in dataset], dtype=np.float32)
if self.standardize_advantages:
mean_advs = xp.mean(advs)
std_advs = xp.std(advs)
advs = (advs - mean_advs) / (std_advs + 1e-8)
# Recompute action distributions for batch backprop
action_distrib = self.policy(states)
log_prob_old = xp.array(
[transition['log_prob'] for transition in dataset],
dtype=np.float32)
gain = self._compute_gain(
log_prob=action_distrib.log_prob(actions),
log_prob_old=log_prob_old,
entropy=action_distrib.entropy,
advs=advs)
# Distribution to compute KL div against
action_distrib_old = action_distrib.copy()
full_step = self._compute_kl_constrained_step(
action_distrib=action_distrib,
action_distrib_old=action_distrib_old,
gain=gain)
self._line_search(
full_step=full_step,
dataset=dataset,
advs=advs,
action_distrib_old=action_distrib_old,
gain=gain)
def _update_policy_recurrent(self, dataset):
"""Update the policy using a given dataset.
The policy is updated via CG and line search.
"""
xp = self.model.xp
flat_transitions = list(itertools.chain.from_iterable(dataset))
# Prepare data for a recurrent model
seqs_states = []
for ep in dataset:
states = self.batch_states(
[transition['state'] for transition in ep], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
seqs_states.append(states)
flat_actions = xp.array(
[transition['action'] for transition in flat_transitions])
flat_advs = xp.array(
[transition['adv'] for transition in flat_transitions],
dtype=np.float32)
if self.standardize_advantages:
mean_advs = xp.mean(flat_advs)
std_advs = xp.std(flat_advs)
flat_advs = (flat_advs - mean_advs) / (std_advs + 1e-8)
with chainer.using_config('train', False),\
chainer.no_backprop_mode():
policy_rs = self.policy.concatenate_recurrent_states(
[ep[0]['recurrent_state'][0] for ep in dataset])
flat_distribs, _ = self.policy.n_step_forward(
seqs_states, recurrent_state=policy_rs, output_mode='concat')
log_prob_old = xp.array(
[transition['log_prob'] for transition in flat_transitions],
dtype=np.float32)
gain = self._compute_gain(
log_prob=flat_distribs.log_prob(flat_actions),
log_prob_old=log_prob_old,
entropy=flat_distribs.entropy,
advs=flat_advs)
# Distribution to compute KL div against
action_distrib_old = flat_distribs.copy()
full_step = self._compute_kl_constrained_step(
action_distrib=flat_distribs,
action_distrib_old=action_distrib_old,
gain=gain)
self._line_search(
full_step=full_step,
dataset=dataset,
advs=flat_advs,
action_distrib_old=action_distrib_old,
gain=gain)
def _compute_kl_constrained_step(self, action_distrib, action_distrib_old,
gain):
"""Compute a step of policy parameters with a KL constraint."""
policy_params = _get_ordered_params(self.policy)
kl = F.mean(action_distrib_old.kl(action_distrib))
# Check if kl computation fully supports double backprop
old_style_funcs = _find_old_style_function([kl])
if old_style_funcs:
raise RuntimeError("""\
Old-style functions (chainer.Function) are used to compute KL divergence.
Since TRPO requires second-order derivative of KL divergence, its computation
should be done with new-style functions (chainer.FunctionNode) only.
Found old-style functions: {}""".format(old_style_funcs))
kl_grads = chainer.grad([kl], policy_params,
enable_double_backprop=True)
assert all(g is not None for g in kl_grads), "\
The gradient contains None. The policy may have unused parameters."
flat_kl_grads = _flatten_and_concat_variables(kl_grads)
def fisher_vector_product_func(vec):
fvp = _hessian_vector_product(flat_kl_grads, policy_params, vec)
return fvp + self.conjugate_gradient_damping * vec
gain_grads = chainer.grad([gain], policy_params)
assert all(g is not None for g in kl_grads), "\
The gradient contains None. The policy may have unused parameters."
flat_gain_grads = _flatten_and_concat_ndarrays(gain_grads)
step_direction = chainerrl.misc.conjugate_gradient(
fisher_vector_product_func, flat_gain_grads,
max_iter=self.conjugate_gradient_max_iter,
)
# We want a step size that satisfies KL(old|new) < max_kl.
# Let d = alpha * step_direction be the actual parameter updates.
# The second-order approximation of KL divergence is:
# KL(old|new) = 1/2 d^T I d + O(||d||^3),
# where I is a Fisher information matrix.
# Substitute d = alpha * step_direction and solve KL(old|new) = max_kl
# for alpha to get the step size that tightly satisfies the constraint.
dId = float(step_direction.dot(
fisher_vector_product_func(step_direction)))
scale = (2.0 * self.max_kl / (dId + 1e-8)) ** 0.5
return scale * step_direction
def _line_search(self, full_step, dataset, advs, action_distrib_old, gain):
"""Do line search for a safe step size."""
xp = self.policy.xp
policy_params = _get_ordered_params(self.policy)
policy_params_sizes = [param.size for param in policy_params]
policy_params_shapes = [param.shape for param in policy_params]
step_size = 1.0
flat_params = _flatten_and_concat_ndarrays(policy_params)
if self.recurrent:
seqs_states = []
for ep in dataset:
states = self.batch_states(
[transition['state'] for transition in ep], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
seqs_states.append(states)
with chainer.using_config('train', False),\
chainer.no_backprop_mode():
policy_rs = self.policy.concatenate_recurrent_states(
[ep[0]['recurrent_state'][0] for ep in dataset])
def evaluate_current_policy():
distrib, _ = self.policy.n_step_forward(
seqs_states, recurrent_state=policy_rs,
output_mode='concat')
return distrib
else:
states = self.batch_states(
[transition['state'] for transition in dataset], xp, self.phi)
if self.obs_normalizer:
states = self.obs_normalizer(states, update=False)
def evaluate_current_policy():
return self.policy(states)
flat_transitions = (list(itertools.chain.from_iterable(dataset))
if self.recurrent else dataset)
actions = xp.array(
[transition['action'] for transition in flat_transitions])
log_prob_old = xp.array(
[transition['log_prob'] for transition in flat_transitions],
dtype=np.float32)
for i in range(self.line_search_max_backtrack + 1):
self.logger.info(
'Line search iteration: %s step size: %s', i, step_size)
new_flat_params = flat_params + step_size * full_step
new_params = _split_and_reshape_to_ndarrays(
new_flat_params,
sizes=policy_params_sizes,
shapes=policy_params_shapes,
)
_replace_params_data(policy_params, new_params)
with chainer.using_config('train', False),\
chainer.no_backprop_mode():
new_action_distrib = evaluate_current_policy()
new_gain = self._compute_gain(
log_prob=new_action_distrib.log_prob(actions),
log_prob_old=log_prob_old,
entropy=new_action_distrib.entropy,
advs=advs)
new_kl = F.mean(action_distrib_old.kl(new_action_distrib))
improve = new_gain.array - gain.array
self.logger.info(
'Surrogate objective improve: %s', float(improve))
self.logger.info('KL divergence: %s', float(new_kl.array))
if not xp.isfinite(new_gain.array):
self.logger.info(
"Surrogate objective is not finite. Bakctracking...")
elif not xp.isfinite(new_kl.array):
self.logger.info(
"KL divergence is not finite. Bakctracking...")
elif improve < 0:
self.logger.info(
"Surrogate objective didn't improve. Bakctracking...")
elif float(new_kl.array) > self.max_kl:
self.logger.info(
"KL divergence exceeds max_kl. Bakctracking...")
else:
self.kl_record.append(float(new_kl.array))
self.policy_step_size_record.append(step_size)
break
step_size *= 0.5
else:
self.logger.info("\
Line search coundn't find a good step size. The policy was not updated.")
self.policy_step_size_record.append(0.)
_replace_params_data(
policy_params,
_split_and_reshape_to_ndarrays(
flat_params,
sizes=policy_params_sizes,
shapes=policy_params_shapes),
)
def act_and_train(self, obs, reward):
if self.last_state is not None:
transition = {
'state': self.last_state,
'action': self.last_action,
'reward': reward,
'next_state': obs,
'nonterminal': 1.0,
}
if self.recurrent:
transition['recurrent_state'] =\
self.model.get_recurrent_state_at(
self.train_prev_recurrent_states,
0, unwrap_variable=True)
self.train_prev_recurrent_states = None
transition['next_recurrent_state'] =\
self.model.get_recurrent_state_at(
self.train_recurrent_states, 0, unwrap_variable=True)
self.last_episode.append(transition)
self._update_if_dataset_is_ready()
xp = self.xp
b_state = self.batch_states([obs], xp, self.phi)
if self.obs_normalizer:
b_state = self.obs_normalizer(b_state, update=False)
# action_distrib will be recomputed when computing gradients
with chainer.using_config('train', False), chainer.no_backprop_mode():
if self.recurrent:
assert self.train_prev_recurrent_states is None
self.train_prev_recurrent_states = self.train_recurrent_states
(action_distrib, value), self.train_recurrent_states =\
self.model(b_state, self.train_prev_recurrent_states)
else:
action_distrib, value = self.model(b_state)
action = chainer.cuda.to_cpu(action_distrib.sample().array)[0]
self.entropy_record.append(float(action_distrib.entropy.array))
self.value_record.append(float(value.array))
self.last_state = obs
self.last_action = action
return action
def act(self, obs):
xp = self.xp
b_state = self.batch_states([obs], xp, self.phi)
if self.obs_normalizer:
b_state = self.obs_normalizer(b_state, update=False)
with chainer.using_config('train', False), chainer.no_backprop_mode():
if self.recurrent:
action_distrib, self.test_recurrent_states =\
self.policy(b_state, self.test_recurrent_states)
else:
action_distrib = self.policy(b_state)
if self.act_deterministically:
action = chainer.cuda.to_cpu(
action_distrib.most_probable.array)[0]
else:
action = chainer.cuda.to_cpu(
action_distrib.sample().array)[0]
return action
def stop_episode_and_train(self, state, reward, done=False):
assert self.last_state is not None
transition = {
'state': self.last_state,
'action': self.last_action,
'reward': reward,
'next_state': state,
'nonterminal': 0.0 if done else 1.0,
}
if self.recurrent:
transition['recurrent_state'] = self.model.get_recurrent_state_at(
self.train_prev_recurrent_states, 0, unwrap_variable=True)
self.train_prev_recurrent_states = None
transition['next_recurrent_state'] =\
self.model.get_recurrent_state_at(
self.train_recurrent_states, 0, unwrap_variable=True)
self.train_recurrent_states = None
self.last_episode.append(transition)
self.last_state = None
self.last_action = None
self._flush_last_episode()
self.stop_episode()
self._update_if_dataset_is_ready()
def stop_episode(self):
self.test_recurrent_states = None
def batch_act(self, batch_obs):
xp = self.xp
b_state = self.batch_states(batch_obs, xp, self.phi)
if self.obs_normalizer:
b_state = self.obs_normalizer(b_state, update=False)
with chainer.using_config('train', False), chainer.no_backprop_mode():
if self.recurrent:
(action_distrib, _), self.test_recurrent_states = self.model(
b_state, self.test_recurrent_states)
else:
action_distrib, _ = self.model(b_state)
if self.act_deterministically:
action = chainer.cuda.to_cpu(
action_distrib.most_probable.array)
else:
action = chainer.cuda.to_cpu(action_distrib.sample().array)
return action
def batch_act_and_train(self, batch_obs):
xp = self.xp
b_state = self.batch_states(batch_obs, xp, self.phi)
if self.obs_normalizer:
b_state = self.obs_normalizer(b_state, update=False)
num_envs = len(batch_obs)
if self.batch_last_episode is None:
self._initialize_batch_variables(num_envs)
assert len(self.batch_last_episode) == num_envs
assert len(self.batch_last_state) == num_envs
assert len(self.batch_last_action) == num_envs
# action_distrib will be recomputed when computing gradients
with chainer.using_config('train', False), chainer.no_backprop_mode():
if self.recurrent:
assert self.train_prev_recurrent_states is None
self.train_prev_recurrent_states = self.train_recurrent_states
(action_distrib, batch_value), self.train_recurrent_states =\
self.model(b_state, self.train_prev_recurrent_states)
else:
action_distrib, batch_value = self.model(b_state)
batch_action = chainer.cuda.to_cpu(action_distrib.sample().array)
self.entropy_record.extend(
chainer.cuda.to_cpu(action_distrib.entropy.array))
self.value_record.extend(chainer.cuda.to_cpu((batch_value.array)))
self.batch_last_state = list(batch_obs)
self.batch_last_action = list(batch_action)
return batch_action
def batch_observe(self, batch_obs, batch_reward, batch_done, batch_reset):
if self.recurrent:
# Reset recurrent states when episodes end
indices_that_ended = [
i for i, (done, reset)
in enumerate(zip(batch_done, batch_reset)) if done or reset]
if indices_that_ended:
self.test_recurrent_states =\
self.model.mask_recurrent_state_at(
self.test_recurrent_states, indices_that_ended)
def batch_observe_and_train(self, batch_obs, batch_reward,
batch_done, batch_reset):
for i, (state, action, reward, next_state, done, reset) in enumerate(zip( # NOQA
self.batch_last_state,
self.batch_last_action,
batch_reward,
batch_obs,
batch_done,
batch_reset,
)):
if state is not None:
assert action is not None
transition = {
'state': state,
'action': action,
'reward': reward,
'next_state': next_state,
'nonterminal': 0.0 if done else 1.0,
}
if self.recurrent:
transition['recurrent_state'] =\
self.model.get_recurrent_state_at(
self.train_prev_recurrent_states,
i, unwrap_variable=True)
transition['next_recurrent_state'] =\
self.model.get_recurrent_state_at(
self.train_recurrent_states,
i, unwrap_variable=True)
self.batch_last_episode[i].append(transition)
if done or reset:
assert self.batch_last_episode[i]
self.memory.append(self.batch_last_episode[i])
self.batch_last_episode[i] = []
self.batch_last_state[i] = None
self.batch_last_action[i] = None
self.train_prev_recurrent_states = None
if self.recurrent:
# Reset recurrent states when episodes end
indices_that_ended = [
i for i, (done, reset)
in enumerate(zip(batch_done, batch_reset)) if done or reset]
if indices_that_ended:
self.train_recurrent_states =\
self.model.mask_recurrent_state_at(
self.train_recurrent_states, indices_that_ended)
self._update_if_dataset_is_ready()
def get_statistics(self):
return [
('average_value', _mean_or_nan(self.value_record)),
('average_entropy', _mean_or_nan(self.entropy_record)),
('average_kl', _mean_or_nan(self.kl_record)),
('average_policy_step_size',
_mean_or_nan(self.policy_step_size_record)),
('explained_variance', self.explained_variance),
]