class Worker(object):
"""
An A3C worker thread. Runs episodes locally and updates global shared value and policy nets.
Args:
name: A unique name for this worker
env: The Gym environment used by this worker
policy_net: Instance of the globally shared policy net
value_net: Instance of the globally shared value net
global_counter: Iterator that holds the global step
discount_factor: Reward discount factor
summary_writer: A tf.train.SummaryWriter for Tensorboard summaries
max_global_steps: If set, stop coordinator when global_counter > max_global_steps
"""
def __init__(self,
name,
env,
policy_net,
value_net,
global_counter,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None):
self.name = name
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
self.summary_writer = summary_writer
self.env = env
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
if LSTM_POLICY:
self.policy_net = LSTMPolicyEstimator(policy_net.num_outputs)
else:
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.value_net = ValueEstimator(reuse=True)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op = make_train_op(self.value_net,
self.global_value_net)
self.pnet_train_op = make_train_op(self.policy_net,
self.global_policy_net)
self.state = None
def run(self, sess, coord, t_max):
with sess.as_default(), sess.graph.as_default():
# Initial state
self.state = self.env.reset()
try:
while not coord.should_stop():
# Copy Parameters from the global networks
sess.run(self.copy_params_op)
# Collect some experience
transitions, local_t, global_t = self.run_n_steps(
t_max, sess)
if self.max_global_steps is not None and global_t >= self.max_global_steps:
tf.logging.info(
"Reached global step {}. Stopping.".format(
global_t))
coord.request_stop()
return
# Update the global networks
self.update(transitions, sess)
except tf.errors.CancelledError:
return
def run_n_steps(self, n, sess):
transitions = []
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
for _ in range(n):
# Take a step
if LSTM_POLICY:
action_probs = self.policy_net.action_inference(self.state)
else:
action_probs = self.policy_net.action_prediction(self.state)
# eps-greedy action
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
next_state, reward, done, _ = self.env.step(action)
# Store transition
transitions.append(
Transition(state=self.state,
action=action,
reward=reward,
next_state=next_state,
done=done))
# Increase local and global counters
local_t = next(self.local_counter)
global_t = next(self.global_counter)
if local_t % 1000 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(
self.name, local_t, global_t))
if done:
self.state = self.env.reset()
### reset features
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
break
else:
self.state = next_state
return transitions, local_t, global_t
def update(self, transitions, sess):
"""
Updates global policy and value networks based on collected experience
Args:
transitions: A list of experience transitions
sess: A Tensorflow session
"""
# If we episode was not done we bootstrap the value from the last state
reward = 0.0
if not transitions[-1].done:
reward = self.value_net.predict_value(transitions[-1].next_state)
if LSTM_POLICY:
init_lstm_state = self.policy_net.get_init_features()
# Accumulate minibatch exmaples
states = []
policy_targets = []
value_targets = []
actions = []
features = []
for transition in transitions[::-1]:
reward = transition.reward + self.discount_factor * reward
policy_target = (reward -
self.value_net.predict_value(transition.state))
# Accumulate updates
states.append(transition.state)
actions.append(transition.action)
policy_targets.append(policy_target)
value_targets.append(reward)
if LSTM_POLICY:
feed_dict = {
self.policy_net.states: np.array(states),
self.policy_net.targets: policy_targets,
self.policy_net.actions: actions,
self.policy_net.state_in[0]: np.array(init_lstm_state[0]),
self.policy_net.state_in[1]: np.array(init_lstm_state[1]),
self.value_net.states: np.array(states),
self.value_net.targets: value_targets,
}
else:
feed_dict = {
self.policy_net.states: np.array(states),
self.policy_net.targets: policy_targets,
self.policy_net.actions: actions,
self.value_net.states: np.array(states),
self.value_net.targets: value_targets,
}
# Train the global estimators using local gradients
global_step, pnet_loss, vnet_loss, _, _, pnet_summaries, vnet_summaries = sess.run(
[
self.global_step, self.policy_net.loss, self.value_net.loss,
self.pnet_train_op, self.vnet_train_op,
self.policy_net.summaries, self.value_net.summaries
], feed_dict)
# Write summaries
if self.summary_writer is not None and global_step % SUMMARY_EACH_STEPS == 0:
self.summary_writer.add_summary(pnet_summaries, global_step)
self.summary_writer.add_summary(vnet_summaries, global_step)
self.summary_writer.flush()
return pnet_loss, vnet_loss, pnet_summaries, vnet_summaries
class PolicyMonitor(object):
"""
Helps evaluating a policy by running an episode in an environment,
saving a video, and plotting summaries to Tensorboard.
Args:
env: environment to run in
policy_net: A policy estimator
summary_writer: a tf.train.SummaryWriter used to write Tensorboard summaries
"""
def __init__(self, env, policy_net, summary_writer, saver=None):
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.env = env
# Correct the path
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
print('[PM] checkpoint_path: {}'.format(self.checkpoint_path))
# Local policy net
with tf.variable_scope("policy_eval"):
if LSTM_POLICY:
self.policy_net = LSTMPolicyEstimator(policy_net.num_outputs)
else:
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def eval_once(self, sess):
with sess.as_default(), sess.graph.as_default():
# Copy params to local model
global_step, _ = sess.run(
[tf.contrib.framework.get_global_step(), self.copy_params_op])
# Run an episode
done = False
state = self.env.reset()
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
total_reward = 0.0
episode_length = 0
while not done:
if LSTM_POLICY:
action_probs = self.policy_net.action_inference(state)
else:
action_probs = self.policy_net.action_prediction(state)
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
next_state, reward, done, _ = self.env.step(action)
# next_state = atari_helpers.atari_make_next_state(state, self.sp.process(next_state))
total_reward += reward
episode_length += 1
state = next_state
# Add summaries
episode_summary = tf.Summary()
episode_summary.value.add(simple_value=total_reward,
tag="eval/total_reward")
episode_summary.value.add(simple_value=episode_length,
tag="eval/episode_length")
self.summary_writer.add_summary(episode_summary, global_step)
self.summary_writer.flush()
if self.saver is not None:
self.saver.save(sess, self.checkpoint_path)
tf.logging.info(
"Eval results at step {}: total_reward {}, episode_length {}".
format(global_step, total_reward, episode_length))
return total_reward, episode_length
def continuous_eval(self, eval_every, sess, coord):
"""
Continuously evaluates the policy every [eval_every] seconds.
"""
try:
while not coord.should_stop():
self.eval_once(sess)
# Sleep until next evaluation cycle
time.sleep(eval_every)
except tf.errors.CancelledError:
return
