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