import time
import numpy as np
import tensorflow as tf
from gym.spaces import Discrete, Box
from stable_baselines import logger
from stable_baselines.a2c.utils import batch_to_seq, seq_to_batch, Scheduler, find_trainable_variables, EpisodeStats, \
get_by_index, check_shape, avg_norm, gradient_add, q_explained_variance, total_episode_reward_logger
from stable_baselines.acer.buffer import Buffer
from stable_baselines.common import ActorCriticRLModel, tf_util, SetVerbosity, TensorboardWriter
from stable_baselines.common.runners import AbstractEnvRunner
from stable_baselines.common.policies import LstmPolicy, ActorCriticPolicy
def strip(var, n_envs, n_steps, flat=False):
"""
Removes the last step in the batch
:param var: (TensorFlow Tensor) The input Tensor
:param n_envs: (int) The number of environments
:param n_steps: (int) The number of steps to run for each environment
:param flat: (bool) If the input Tensor is flat
:return: (TensorFlow Tensor) the input tensor, without the last step in the batch
"""
out_vars = batch_to_seq(var, n_envs, n_steps + 1, flat)
return seq_to_batch(out_vars[:-1], flat)
def q_retrace(rewards, dones, q_i, values, rho_i, n_envs, n_steps, gamma):
"""
Calculates the target Q-retrace
:param rewards: ([TensorFlow Tensor]) The rewards
:param dones: ([TensorFlow Tensor])
:param q_i: ([TensorFlow Tensor]) The Q values for actions taken
:param values: ([TensorFlow Tensor]) The output of the value functions
:param rho_i: ([TensorFlow Tensor]) The importance weight for each action
:param n_envs: (int) The number of environments
:param n_steps: (int) The number of steps to run for each environment
:param gamma: (float) The discount value
:return: ([TensorFlow Tensor]) the target Q-retrace
"""
rho_bar = batch_to_seq(tf.minimum(1.0, rho_i), n_envs, n_steps, True) # list of len steps, shape [n_envs]
reward_seq = batch_to_seq(rewards, n_envs, n_steps, True) # list of len steps, shape [n_envs]
done_seq = batch_to_seq(dones, n_envs, n_steps, True) # list of len steps, shape [n_envs]
q_is = batch_to_seq(q_i, n_envs, n_steps, True)
value_sequence = batch_to_seq(values, n_envs, n_steps + 1, True)
final_value = value_sequence[-1]
qret = final_value
qrets = []
for i in range(n_steps - 1, -1, -1):
check_shape([qret, done_seq[i], reward_seq[i], rho_bar[i], q_is[i], value_sequence[i]], [[n_envs]] * 6)
qret = reward_seq[i] + gamma * qret * (1.0 - done_seq[i])
qrets.append(qret)
qret = (rho_bar[i] * (qret - q_is[i])) + value_sequence[i]
qrets = qrets[::-1]
qret = seq_to_batch(qrets, flat=True)
return qret
[docs]class ACER(ActorCriticRLModel):
"""
The ACER (Actor-Critic with Experience Replay) model class, https://arxiv.org/abs/1611.01224
:param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param gamma: (float) The discount value
:param n_steps: (int) The number of steps to run for each environment per update
(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
:param num_procs: (int) The number of threads for TensorFlow operations
:param q_coef: (float) The weight for the loss on the Q value
:param ent_coef: (float) The weight for the entropic loss
:param max_grad_norm: (float) The clipping value for the maximum gradient
:param learning_rate: (float) The initial learning rate for the RMS prop optimizer
:param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant',
'double_linear_con', 'middle_drop' or 'double_middle_drop')
:param rprop_epsilon: (float) RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update)
(default: 1e-5)
:param rprop_alpha: (float) RMSProp decay parameter (default: 0.99)
:param buffer_size: (int) The buffer size in number of steps
:param replay_ratio: (float) The number of replay learning per on policy learning on average,
using a poisson distribution
:param replay_start: (int) The minimum number of steps in the buffer, before learning replay
:param correction_term: (float) Importance weight clipping factor (default: 10)
:param trust_region: (bool) Whether or not algorithms estimates the gradient KL divergence
between the old and updated policy and uses it to determine step size (default: True)
:param alpha: (float) The decay rate for the Exponential moving average of the parameters
:param delta: (float) max KL divergence between the old policy and updated policy (default: 1)
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
"""
def __init__(self, policy, env, gamma=0.99, n_steps=20, num_procs=1, q_coef=0.5, ent_coef=0.01, max_grad_norm=10,
learning_rate=7e-4, lr_schedule='linear', rprop_alpha=0.99, rprop_epsilon=1e-5, buffer_size=5000,
replay_ratio=4, replay_start=1000, correction_term=10.0, trust_region=True, alpha=0.99, delta=1,
verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None):
super(ACER, self).__init__(policy=policy, env=env, verbose=verbose, requires_vec_env=True,
_init_setup_model=_init_setup_model, policy_kwargs=policy_kwargs)
self.n_steps = n_steps
self.replay_ratio = replay_ratio
self.buffer_size = buffer_size
self.replay_start = replay_start
self.gamma = gamma
self.alpha = alpha
self.correction_term = correction_term
self.q_coef = q_coef
self.ent_coef = ent_coef
self.trust_region = trust_region
self.delta = delta
self.max_grad_norm = max_grad_norm
self.rprop_alpha = rprop_alpha
self.rprop_epsilon = rprop_epsilon
self.learning_rate = learning_rate
self.lr_schedule = lr_schedule
self.num_procs = num_procs
self.tensorboard_log = tensorboard_log
self.graph = None
self.sess = None
self.action_ph = None
self.done_ph = None
self.reward_ph = None
self.mu_ph = None
self.learning_rate_ph = None
self.params = None
self.polyak_model = None
self.learning_rate_schedule = None
self.run_ops = None
self.names_ops = None
self.train_model = None
self.step_model = None
self.step = None
self.proba_step = None
self.initial_state = None
self.n_act = None
self.n_batch = None
self.summary = None
self.episode_reward = None
if _init_setup_model:
self.setup_model()
[docs] def set_env(self, env):
if env is not None:
assert self.n_envs == env.num_envs, \
"Error: the environment passed must have the same number of environments as the model was trained on." \
"This is due to ACER not being capable of changing the number of environments."
super().set_env(env)
[docs] def setup_model(self):
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the ACER model must be " \
"an instance of common.policies.ActorCriticPolicy."
if isinstance(self.action_space, Discrete):
self.n_act = self.action_space.n
continuous = False
elif isinstance(self.action_space, Box):
# self.n_act = self.action_space.shape[-1]
# continuous = True
raise NotImplementedError("WIP: Acer does not support Continuous actions yet.")
else:
raise ValueError("Error: ACER does not work with {} actions space.".format(self.action_space))
self.n_batch = self.n_envs * self.n_steps
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.make_session(num_cpu=self.num_procs, graph=self.graph)
n_batch_step = None
if issubclass(self.policy, LstmPolicy):
n_batch_step = self.n_envs
n_batch_train = self.n_envs * (self.n_steps + 1)
step_model = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
n_batch_step, reuse=False, **self.policy_kwargs)
self.params = find_trainable_variables("model")
with tf.variable_scope("train_model", reuse=True,
custom_getter=tf_util.outer_scope_getter("train_model")):
train_model = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs,
self.n_steps + 1, n_batch_train, reuse=True, **self.policy_kwargs)
with tf.variable_scope("moving_average"):
# create averaged model
ema = tf.train.ExponentialMovingAverage(self.alpha)
ema_apply_op = ema.apply(self.params)
def custom_getter(getter, name, *args, **kwargs):
name = name.replace("polyak_model/", "")
val = ema.average(getter(name, *args, **kwargs))
return val
with tf.variable_scope("polyak_model", reuse=True, custom_getter=custom_getter):
self.polyak_model = polyak_model = self.policy(self.sess, self.observation_space, self.action_space,
self.n_envs, self.n_steps + 1,
self.n_envs * (self.n_steps + 1), reuse=True,
**self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
self.done_ph = tf.placeholder(tf.float32, [self.n_batch]) # dones
self.reward_ph = tf.placeholder(tf.float32, [self.n_batch]) # rewards, not returns
self.mu_ph = tf.placeholder(tf.float32, [self.n_batch, self.n_act]) # mu's
self.action_ph = train_model.pdtype.sample_placeholder([self.n_batch])
self.learning_rate_ph = tf.placeholder(tf.float32, [])
eps = 1e-6
# Notation: (var) = batch variable, (var)s = sequence variable,
# (var)_i = variable index by action at step i
# shape is [n_envs * (n_steps + 1)]
if continuous:
value = train_model.value_fn[:, 0]
else:
value = tf.reduce_sum(train_model.policy_proba * train_model.q_value, axis=-1)
rho, rho_i_ = None, None
if continuous:
action_ = strip(train_model.proba_distribution.sample(), self.n_envs, self.n_steps)
distribution_f = tf.contrib.distributions.MultivariateNormalDiag(
loc=strip(train_model.proba_distribution.mean, self.n_envs, self.n_steps),
scale_diag=strip(train_model.proba_distribution.logstd, self.n_envs, self.n_steps))
f_polyak = tf.contrib.distributions.MultivariateNormalDiag(
loc=strip(polyak_model.proba_distribution.mean, self.n_envs, self.n_steps),
scale_diag=strip(polyak_model.proba_distribution.logstd, self.n_envs, self.n_steps))
f_i = distribution_f.prob(self.action_ph)
f_i_ = distribution_f.prob(action_)
f_polyak_i = f_polyak.prob(self.action_ph)
phi_i = strip(train_model.proba_distribution.mean, self.n_envs, self.n_steps)
q_value = strip(train_model.value_fn, self.n_envs, self.n_steps)
q_i = q_value[:, 0]
rho_i = tf.reshape(f_i, [-1, 1]) / (self.mu_ph + eps)
rho_i_ = tf.reshape(f_i_, [-1, 1]) / (self.mu_ph + eps)
qret = q_retrace(self.reward_ph, self.done_ph, q_i, value, tf.pow(rho_i, 1/self.n_act),
self.n_envs, self.n_steps, self.gamma)
else:
# strip off last step
# f is a distribution, chosen to be Gaussian distributions
# with fixed diagonal covariance and mean \phi(x)
# in the paper
distribution_f, f_polyak, q_value = \
map(lambda variables: strip(variables, self.n_envs, self.n_steps),
[train_model.policy_proba, polyak_model.policy_proba, train_model.q_value])
# Get pi and q values for actions taken
f_i = get_by_index(distribution_f, self.action_ph)
f_i_ = distribution_f
phi_i = distribution_f
f_polyak_i = f_polyak
q_i = get_by_index(q_value, self.action_ph)
# Compute ratios for importance truncation
rho = distribution_f / (self.mu_ph + eps)
rho_i = get_by_index(rho, self.action_ph)
# Calculate Q_retrace targets
qret = q_retrace(self.reward_ph, self.done_ph, q_i, value, rho_i, self.n_envs, self.n_steps,
self.gamma)
# Calculate losses
# Entropy
entropy = tf.reduce_sum(train_model.proba_distribution.entropy())
# Policy Gradient loss, with truncated importance sampling & bias correction
value = strip(value, self.n_envs, self.n_steps, True)
# check_shape([qret, value, rho_i, f_i], [[self.n_envs * self.n_steps]] * 4)
# check_shape([rho, distribution_f, q_value], [[self.n_envs * self.n_steps, self.n_act]] * 2)
# Truncated importance sampling
adv = qret - value
log_f = tf.log(f_i + eps)
# [n_envs * n_steps]
gain_f = log_f * tf.stop_gradient(adv * tf.minimum(self.correction_term, rho_i))
loss_f = -tf.reduce_mean(gain_f)
# Bias correction for the truncation
adv_bc = (q_value - tf.reshape(value, [self.n_envs * self.n_steps, 1])) # [n_envs * n_steps, n_act]
# check_shape([adv_bc, log_f_bc], [[self.n_envs * self.n_steps, self.n_act]] * 2)
if continuous:
gain_bc = tf.stop_gradient(adv_bc *
tf.nn.relu(1.0 - (self.correction_term / (rho_i_ + eps))) *
f_i_)
else:
log_f_bc = tf.log(f_i_ + eps) # / (f_old + eps)
gain_bc = tf.reduce_sum(log_f_bc *
tf.stop_gradient(
adv_bc *
tf.nn.relu(1.0 - (self.correction_term / (rho + eps))) *
f_i_),
axis=1)
# IMP: This is sum, as expectation wrt f
loss_bc = -tf.reduce_mean(gain_bc)
loss_policy = loss_f + loss_bc
# Value/Q function loss, and explained variance
check_shape([qret, q_i], [[self.n_envs * self.n_steps]] * 2)
explained_variance = q_explained_variance(tf.reshape(q_i, [self.n_envs, self.n_steps]),
tf.reshape(qret, [self.n_envs, self.n_steps]))
loss_q = tf.reduce_mean(tf.square(tf.stop_gradient(qret) - q_i) * 0.5)
# Net loss
check_shape([loss_policy, loss_q, entropy], [[]] * 3)
loss = loss_policy + self.q_coef * loss_q - self.ent_coef * entropy
tf.summary.scalar('entropy_loss', entropy)
tf.summary.scalar('policy_gradient_loss', loss_policy)
tf.summary.scalar('value_function_loss', loss_q)
tf.summary.scalar('loss', loss)
norm_grads_q, norm_grads_policy, avg_norm_grads_f = None, None, None
avg_norm_k, avg_norm_g, avg_norm_k_dot_g, avg_norm_adj = None, None, None, None
if self.trust_region:
# [n_envs * n_steps, n_act]
grad = tf.gradients(- (loss_policy - self.ent_coef * entropy) * self.n_steps * self.n_envs,
phi_i)
# [n_envs * n_steps, n_act] # Directly computed gradient of KL divergence wrt f
kl_grad = - f_polyak_i / (f_i_ + eps)
k_dot_g = tf.reduce_sum(kl_grad * grad, axis=-1)
adj = tf.maximum(0.0, (tf.reduce_sum(kl_grad * grad, axis=-1) - self.delta) / (
tf.reduce_sum(tf.square(kl_grad), axis=-1) + eps)) # [n_envs * n_steps]
# Calculate stats (before doing adjustment) for logging.
avg_norm_k = avg_norm(kl_grad)
avg_norm_g = avg_norm(grad)
avg_norm_k_dot_g = tf.reduce_mean(tf.abs(k_dot_g))
avg_norm_adj = tf.reduce_mean(tf.abs(adj))
grad = grad - tf.reshape(adj, [self.n_envs * self.n_steps, 1]) * kl_grad
# These are turst region adjusted gradients wrt f ie statistics of policy pi
grads_f = -grad / (self.n_envs * self.n_steps)
grads_policy = tf.gradients(f_i_, self.params, grads_f)
grads_q = tf.gradients(loss_q * self.q_coef, self.params)
grads = [gradient_add(g1, g2, param, verbose=self.verbose)
for (g1, g2, param) in zip(grads_policy, grads_q, self.params)]
avg_norm_grads_f = avg_norm(grads_f) * (self.n_steps * self.n_envs)
norm_grads_q = tf.global_norm(grads_q)
norm_grads_policy = tf.global_norm(grads_policy)
else:
grads = tf.gradients(loss, self.params)
norm_grads = None
if self.max_grad_norm is not None:
grads, norm_grads = tf.clip_by_global_norm(grads, self.max_grad_norm)
grads = list(zip(grads, self.params))
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('rewards', tf.reduce_mean(self.reward_ph))
tf.summary.histogram('rewards', self.reward_ph)
tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate))
tf.summary.histogram('learning_rate', self.learning_rate)
tf.summary.scalar('advantage', tf.reduce_mean(adv))
tf.summary.histogram('advantage', adv)
tf.summary.scalar('action_probabilty', tf.reduce_mean(self.mu_ph))
tf.summary.histogram('action_probabilty', self.mu_ph)
if len(self.observation_space.shape) == 3:
tf.summary.image('observation', train_model.obs_ph)
else:
tf.summary.histogram('observation', train_model.obs_ph)
trainer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate_ph, decay=self.rprop_alpha,
epsilon=self.rprop_epsilon)
_opt_op = trainer.apply_gradients(grads)
# so when you call _train, you first do the gradient step, then you apply ema
with tf.control_dependencies([_opt_op]):
_train = tf.group(ema_apply_op)
# Ops/Summaries to run, and their names for logging
assert norm_grads is not None
run_ops = [_train, loss, loss_q, entropy, loss_policy, loss_f, loss_bc, explained_variance, norm_grads]
names_ops = ['loss', 'loss_q', 'entropy', 'loss_policy', 'loss_f', 'loss_bc', 'explained_variance',
'norm_grads']
if self.trust_region:
self.run_ops = run_ops + [norm_grads_q, norm_grads_policy, avg_norm_grads_f, avg_norm_k, avg_norm_g,
avg_norm_k_dot_g, avg_norm_adj]
self.names_ops = names_ops + ['norm_grads_q', 'norm_grads_policy', 'avg_norm_grads_f', 'avg_norm_k',
'avg_norm_g', 'avg_norm_k_dot_g', 'avg_norm_adj']
self.train_model = train_model
self.step_model = step_model
self.step = step_model.step
self.proba_step = step_model.proba_step
self.initial_state = step_model.initial_state
tf.global_variables_initializer().run(session=self.sess)
self.summary = tf.summary.merge_all()
def _train_step(self, obs, actions, rewards, dones, mus, states, masks, steps, writer=None):
"""
applies a training step to the model
:param obs: ([float]) The input observations
:param actions: ([float]) The actions taken
:param rewards: ([float]) The rewards from the environment
:param dones: ([bool]) Whether or not the episode is over (aligned with reward, used for reward calculation)
:param mus: ([float]) The logits values
:param states: ([float]) The states (used for recurrent policies)
:param masks: ([bool]) Whether or not the episode is over (used for recurrent policies)
:param steps: (int) the number of steps done so far (can be None)
:param writer: (TensorFlow Summary.writer) the writer for tensorboard
:return: ([str], [float]) the list of update operation name, and the list of the results of the operations
"""
cur_lr = self.learning_rate_schedule.value_steps(steps)
td_map = {self.train_model.obs_ph: obs, self.polyak_model.obs_ph: obs, self.action_ph: actions,
self.reward_ph: rewards, self.done_ph: dones, self.mu_ph: mus, self.learning_rate_ph: cur_lr}
if states is not None:
td_map[self.train_model.states_ph] = states
td_map[self.train_model.masks_ph] = masks
td_map[self.polyak_model.states_ph] = states
td_map[self.polyak_model.masks_ph] = masks
if writer is not None:
# run loss backprop with summary, but once every 10 runs save the metadata (memory, compute time, ...)
if (1 + (steps / self.n_batch)) % 10 == 0:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
step_return = self.sess.run([self.summary] + self.run_ops, td_map, options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(run_metadata, 'step%d' % steps)
else:
step_return = self.sess.run([self.summary] + self.run_ops, td_map)
writer.add_summary(step_return[0], steps)
step_return = step_return[1:]
else:
step_return = self.sess.run(self.run_ops, td_map)
return self.names_ops, step_return[1:] # strip off _train
[docs] def learn(self, total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name="ACER"):
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name) as writer:
self._setup_learn(seed)
self.learning_rate_schedule = Scheduler(initial_value=self.learning_rate, n_values=total_timesteps,
schedule=self.lr_schedule)
episode_stats = EpisodeStats(self.n_steps, self.n_envs)
runner = _Runner(env=self.env, model=self, n_steps=self.n_steps)
self.episode_reward = np.zeros((self.n_envs,))
if self.replay_ratio > 0:
buffer = Buffer(env=self.env, n_steps=self.n_steps, size=self.buffer_size)
else:
buffer = None
t_start = time.time()
# n_batch samples, 1 on_policy call and multiple off-policy calls
for steps in range(0, total_timesteps, self.n_batch):
enc_obs, obs, actions, rewards, mus, dones, masks = runner.run()
episode_stats.feed(rewards, dones)
if buffer is not None:
buffer.put(enc_obs, actions, rewards, mus, dones, masks)
if writer is not None:
self.episode_reward = total_episode_reward_logger(self.episode_reward,
rewards.reshape((self.n_envs, self.n_steps)),
dones.reshape((self.n_envs, self.n_steps)),
writer, steps)
# reshape stuff correctly
obs = obs.reshape(runner.batch_ob_shape)
actions = actions.reshape([runner.n_batch])
rewards = rewards.reshape([runner.n_batch])
mus = mus.reshape([runner.n_batch, runner.n_act])
dones = dones.reshape([runner.n_batch])
masks = masks.reshape([runner.batch_ob_shape[0]])
names_ops, values_ops = self._train_step(obs, actions, rewards, dones, mus, self.initial_state, masks,
steps, writer)
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) == False:
break
if self.verbose >= 1 and (int(steps / runner.n_batch) % log_interval == 0):
logger.record_tabular("total_timesteps", steps)
logger.record_tabular("fps", int(steps / (time.time() - t_start)))
# IMP: In EpisodicLife env, during training, we get done=True at each loss of life,
# not just at the terminal state. Thus, this is mean until end of life, not end of episode.
# For true episode rewards, see the monitor files in the log folder.
logger.record_tabular("mean_episode_length", episode_stats.mean_length())
logger.record_tabular("mean_episode_reward", episode_stats.mean_reward())
for name, val in zip(names_ops, values_ops):
logger.record_tabular(name, float(val))
logger.dump_tabular()
if self.replay_ratio > 0 and buffer.has_atleast(self.replay_start):
samples_number = np.random.poisson(self.replay_ratio)
for _ in range(samples_number):
# get obs, actions, rewards, mus, dones from buffer.
obs, actions, rewards, mus, dones, masks = buffer.get()
# reshape stuff correctly
obs = obs.reshape(runner.batch_ob_shape)
actions = actions.reshape([runner.n_batch])
rewards = rewards.reshape([runner.n_batch])
mus = mus.reshape([runner.n_batch, runner.n_act])
dones = dones.reshape([runner.n_batch])
masks = masks.reshape([runner.batch_ob_shape[0]])
self._train_step(obs, actions, rewards, dones, mus, self.initial_state, masks, steps)
return self
[docs] def save(self, save_path):
data = {
"gamma": self.gamma,
"n_steps": self.n_steps,
"q_coef": self.q_coef,
"ent_coef": self.ent_coef,
"max_grad_norm": self.max_grad_norm,
"learning_rate": self.learning_rate,
"lr_schedule": self.lr_schedule,
"rprop_alpha": self.rprop_alpha,
"rprop_epsilon": self.rprop_epsilon,
"replay_ratio": self.replay_ratio,
"replay_start": self.replay_start,
"verbose": self.verbose,
"policy": self.policy,
"observation_space": self.observation_space,
"action_space": self.action_space,
"n_envs": self.n_envs,
"_vectorize_action": self._vectorize_action,
"policy_kwargs": self.policy_kwargs
}
params = self.sess.run(self.params)
self._save_to_file(save_path, data=data, params=params)
class _Runner(AbstractEnvRunner):
def __init__(self, env, model, n_steps):
"""
A runner to learn the policy of an environment for a model
:param env: (Gym environment) The environment to learn from
:param model: (Model) The model to learn
:param n_steps: (int) The number of steps to run for each environment
"""
super(_Runner, self).__init__(env=env, model=model, n_steps=n_steps)
self.env = env
self.model = model
self.n_env = n_env = env.num_envs
if isinstance(env.action_space, Discrete):
self.n_act = env.action_space.n
else:
self.n_act = env.action_space.shape[-1]
self.n_batch = n_env * n_steps
if len(env.observation_space.shape) > 1:
self.raw_pixels = True
obs_height, obs_width, obs_num_channels = env.observation_space.shape
self.batch_ob_shape = (n_env * (n_steps + 1), obs_height, obs_width, obs_num_channels)
self.obs_dtype = np.uint8
self.obs = np.zeros((n_env, obs_height, obs_width, obs_num_channels), dtype=self.obs_dtype)
self.num_channels = obs_num_channels
else:
if len(env.observation_space.shape) == 1:
self.obs_dim = env.observation_space.shape[0]
else:
self.obs_dim = 1
self.raw_pixels = False
if isinstance(self.env.observation_space, Discrete):
self.batch_ob_shape = (n_env * (n_steps + 1),)
else:
self.batch_ob_shape = (n_env * (n_steps + 1), self.obs_dim)
self.obs_dtype = np.float32
self.n_steps = n_steps
self.states = model.initial_state
self.dones = [False for _ in range(n_env)]
def run(self):
"""
Run a step leaning of the model
:return: ([float], [float], [float], [float], [float], [bool], [float])
encoded observation, observations, actions, rewards, mus, dones, masks
"""
enc_obs = [self.obs]
mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], []
for _ in range(self.n_steps):
actions, _, states, _ = self.model.step(self.obs, self.states, self.dones)
mus = self.model.proba_step(self.obs, self.states, self.dones)
mb_obs.append(np.copy(self.obs))
mb_actions.append(actions)
mb_mus.append(mus)
mb_dones.append(self.dones)
clipped_actions = actions
# Clip the actions to avoid out of bound error
if isinstance(self.env.action_space, Box):
clipped_actions = np.clip(actions, self.env.action_space.low, self.env.action_space.high)
obs, rewards, dones, _ = self.env.step(clipped_actions)
# states information for statefull models like LSTM
self.states = states
self.dones = dones
self.obs = obs
mb_rewards.append(rewards)
enc_obs.append(obs)
mb_obs.append(np.copy(self.obs))
mb_dones.append(self.dones)
enc_obs = np.asarray(enc_obs, dtype=self.obs_dtype).swapaxes(1, 0)
mb_obs = np.asarray(mb_obs, dtype=self.obs_dtype).swapaxes(1, 0)
mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)
mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done
mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards
# shapes are now [nenv, nsteps, []]
# When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy.
return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks