def run_n_steps(self, n, sess):
transitions = []
for _ in range(n):
# Take a step
action_probs = self._policy_net_predict(self.state, sess)
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(
self.state, self.sp.process(next_state))
# 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 % 100 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(
self.name, local_t, global_t))
if done:
self.state = atari_helpers.atari_make_initial_state(
self.sp.process(self.env.reset()))
break
else:
self.state = next_state
return transitions, local_t, global_t
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 = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))
total_reward = 0.0
episode_length = 0
while not done:
action_probs = self._policy_net_predict(state, sess)
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))
f_reward.write(str(global_step) + " " + str(total_reward) + " " + str(episode_length) + "\n")
return total_reward, episode_length
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 = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))
total_reward = 0.0
episode_length = 0
while not done:
action_probs = self._policy_net_predict(state, sess)
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 run(self, sess, coord, t_max):
with sess.as_default(), sess.graph.as_default():
# Initial state
self.state = atari_helpers.atari_make_initial_state(
self.sp.process(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 = []
for _ in range(n):
# Take a step
action_probs = self._policy_net_predict(self.state, sess)
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(self.state, self.sp.process(next_state))
# 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 % 100 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(self.name, local_t, global_t))
if done:
self.state = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))
break
else:
self.state = next_state
return transitions, local_t, global_t
def testPredict(self):
env = make_env()
sp = StateProcessor()
estimator = PolicyEstimator(len(VALID_ACTIONS))
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
# Generate a state
state = sp.process(env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
processed_states = np.array([processed_state])
# Run feeds
feed_dict = {
estimator.states: processed_states,
estimator.targets: [1.0],
estimator.actions: [1]
}
loss = sess.run(estimator.loss, feed_dict)
pred = sess.run(estimator.predictions, feed_dict)
# Assertions
self.assertTrue(loss != 0.0)
self.assertEqual(pred["probs"].shape, (1, len(VALID_ACTIONS)))
self.assertEqual(pred["logits"].shape, (1, len(VALID_ACTIONS)))
def testGradient(self):
env = make_env()
sp = StateProcessor()
estimator = PolicyEstimator(len(VALID_ACTIONS))
grads = [g for g, _ in estimator.grads_and_vars]
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
# Generate a state
state = sp.process(env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
processed_states = np.array([processed_state])
# Run feeds to get gradients
feed_dict = {
estimator.states: processed_states,
estimator.targets: [1.0],
estimator.actions: [1]
}
grads_ = sess.run(grads, feed_dict)
# Apply calculated gradients
grad_feed_dict = {k: v for k, v in zip(grads, grads_)}
_ = sess.run(estimator.train_op, grad_feed_dict)
def run_n_steps(self, n, sess):
transitions = []
for _ in range(n):
# Take a step
action_probs = self._policy_net_predict(self.state, sess)
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(self.state, self.sp.process(next_state))
self.total_reward += reward
self.episode_length += 1
# 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 % 100 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(self.name, local_t, global_t))
if done:
self.state = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))
f = open('logs_policy.out', 'a')
f.write("agent {}, local {}, global {}, total_reward {}, episode_length {}\n".format(
self.name, self.local_counter, self.global_counter, self.total_reward, self.episode_length))
f.close()
self.total_reward = 0
self.episode_length = 0
break
else:
self.state = next_state
return transitions, local_t, global_t
def run_n_steps(self, n, sess):
transitions = []
for _ in range(n):
# Take a step
action_probs = self._policy_net_predict(self.state, sess)
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
repetition_probs = self._repetition_net_predict(self.state, sess)
repetition = np.random.choice(np.arange(len(repetition_probs)),
p=repetition_probs)
rewards_collected = []
# print("repetition", self.name,repetition)
for rep in range(repetition + 1):
next_state, reward, done, _ = self.env.step(action)
# print(self.name,rep)
# print("action",action)
next_state = atari_helpers.atari_make_next_state(
self.state, self.sp.process(next_state))
rewards_collected.append(reward)
# Increase local and global counters
local_t = next(self.local_counter)
global_t = next(self.global_counter)
if local_t % 100 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(
self.name, local_t, global_t))
if done:
transitions.append(
Transition(state=self.state,
action=action,
repetition=repetition,
reward=sum(rewards_collected) /
len(rewards_collected),
next_state=next_state,
done=done))
self.state = atari_helpers.atari_make_initial_state(
self.sp.process(self.env.reset()))
break
else:
if rep == repetition:
transitions.append(
Transition(state=self.state,
action=action,
repetition=repetition,
reward=sum(rewards_collected) /
len(rewards_collected),
next_state=next_state,
done=done))
self.state = next_state
return transitions, local_t, global_t
def testValueNetPredict(self):
w = Worker(name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
state_value = w._value_net_predict(processed_state, sess)
self.assertEqual(state_value.shape, ())
def testValueNetPredict(self):
w = Worker(
name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
state_value = w._value_net_predict(processed_state, sess)
self.assertEqual(state_value.shape, ())
def run(self, sess, coord, t_max):
with sess.as_default(), sess.graph.as_default():
# Initial state
self.state = atari_helpers.atari_make_initial_state(self.sp.process(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 testRunNStepsAndUpdate(self):
w = Worker(name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
w.state = processed_state
transitions, local_t, global_t = w.run_n_steps(10, sess)
policy_net_loss, value_net_loss, policy_net_summaries, value_net_summaries = w.update(
transitions, sess)
self.assertEqual(len(transitions), 10)
self.assertIsNotNone(policy_net_loss)
self.assertIsNotNone(value_net_loss)
self.assertIsNotNone(policy_net_summaries)
self.assertIsNotNone(value_net_summaries)
def testRunNStepsAndUpdate(self):
w = Worker(
name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
w.state = processed_state
transitions, local_t, global_t = w.run_n_steps(10, sess)
policy_net_loss, value_net_loss, policy_net_summaries, value_net_summaries = w.update(transitions, sess)
self.assertEqual(len(transitions), 10)
self.assertIsNotNone(policy_net_loss)
self.assertIsNotNone(value_net_loss)
self.assertIsNotNone(policy_net_summaries)
self.assertIsNotNone(value_net_summaries)
class Worker(object):
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_step
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.sp = StateProcessor()
self.summary_writer = summary_writer
self.env = env
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.value_net = ValueEstimator(reuse=True)
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+'/',
ollection=tf.GraphKeys.TRAINABLE_VARIABLES)
)
self.vnet_train_op = make_copy_params_op(
self.value_net, self.global_value_net)
self.pnet_train_op = make_copy_params_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():
self.state = atari_helpers.atari_make_initial_state(
self.sp.process(self.env.reset()))
try:
while not coord.should_stop():
sess.run(self.copy_params_op)
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
self.update(transitions, sess)
except tf.errors.CancelledError:
return
def _policy_net_predict(self, state, sess):
feed_dic = {self.policy_net.states: [states]}
preds = sess.run(self.policy_net.predictions, feed_dic)
return preds["probs"][0]
def _value_net_predict(self, state, sess):
feed_dict = {self.value_net.states: [state]}
preds = sess.run(self.value_net.predictions, feed_dict)
return preds["logits"][0]
def run_n_steps(self, n, sess):
transitions = []
for _ in range(n):
action_probes = self._policy_net_predict(self.state, sess)
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(
self.state, self.sp.process(next_state))
transitions.append(Transition(s
tate=self.state,
action=action,
reward=reward,
next_state=next_state,
done=done))
local_t = next(self.local_counter)
global_t = next(self.global_counter)
if local_t % 100 == 0:
tf.logging.info("{}: local step {}, global step {}". format(
self.name, local_t, global_t))
if done:
self.state = atari_helpers.atari_make_initial_state(
self.sp.process(self.env.reset()))
break
else:
self.state = next_state
