def testActionsWithinRange(self): observation_spec = tensor_spec.BoundedTensorSpec((8, 8, 3), tf.float32, 0, 1) time_step_spec = ts.time_step_spec(observation_spec) time_step = tensor_spec.sample_spec_nest(time_step_spec, outer_dims=(1, )) action_spec = [ tensor_spec.BoundedTensorSpec((2, ), tf.float32, 2, 3), tensor_spec.BoundedTensorSpec((3, ), tf.float32, 0, 3) ] net = actor_rnn_network.ActorRnnNetwork(observation_spec, action_spec, conv_layer_params=[(4, 2, 2)], input_fc_layer_params=(5, ), output_fc_layer_params=(5, ), lstm_size=(3, )) actions, _ = net(time_step.observation, time_step.step_type) self.evaluate(tf.compat.v1.global_variables_initializer()) for (action, spec) in zip(actions, action_spec): action_ = self.evaluate(action) self.assertTrue(np.all(action_ >= spec.minimum)) self.assertTrue(np.all(action_ <= spec.maximum))
def testBuilds(self): observation_spec = tensor_spec.BoundedTensorSpec((8, 8, 3), tf.float32, 0, 1) time_step_spec = ts.time_step_spec(observation_spec) time_step = tensor_spec.sample_spec_nest(time_step_spec, outer_dims=(1, )) action_spec = [ tensor_spec.BoundedTensorSpec((2, ), tf.float32, 2, 3), tensor_spec.BoundedTensorSpec((3, ), tf.float32, 0, 3) ] net = actor_rnn_network.ActorRnnNetwork(observation_spec, action_spec, conv_layer_params=[(4, 2, 2)], input_fc_layer_params=(5, ), lstm_size=(3, ), output_fc_layer_params=(5, )) actions, network_state = net(time_step.observation, time_step.step_type, net.get_initial_state(batch_size=1)) self.evaluate(tf.compat.v1.global_variables_initializer()) self.assertEqual([1, 2], actions[0].shape.as_list()) self.assertEqual([1, 3], actions[1].shape.as_list()) self.assertEqual(13, len(net.variables)) # Conv Net Kernel self.assertEqual((2, 2, 3, 4), net.variables[0].shape) # Conv Net bias self.assertEqual((4, ), net.variables[1].shape) # Fc Kernel self.assertEqual((64, 5), net.variables[2].shape) # Fc Bias self.assertEqual((5, ), net.variables[3].shape) # LSTM Cell Kernel self.assertEqual((5, 12), net.variables[4].shape) # LSTM Cell Recurrent Kernel self.assertEqual((3, 12), net.variables[5].shape) # LSTM Cell Bias self.assertEqual((12, ), net.variables[6].shape) # Fc Kernel self.assertEqual((3, 5), net.variables[7].shape) # Fc Bias self.assertEqual((5, ), net.variables[8].shape) # Action 1 Kernel self.assertEqual((5, 2), net.variables[9].shape) # Action 1 Bias self.assertEqual((2, ), net.variables[10].shape) # Action 2 Kernel self.assertEqual((5, 3), net.variables[11].shape) # Action 2 Bias self.assertEqual((3, ), net.variables[12].shape) # Assert LSTM cell is created. self.assertEqual((1, 3), network_state[0].shape) self.assertEqual((1, 3), network_state[1].shape)
def train_eval( root_dir, env_name='cartpole', task_name='balance', observations_whitelist='position', num_iterations=100000, actor_fc_layers=(400, 300), actor_output_fc_layers=(100, ), actor_lstm_size=(40, ), critic_obs_fc_layers=(400, ), critic_action_fc_layers=None, critic_joint_fc_layers=(300, ), critic_output_fc_layers=(100, ), critic_lstm_size=(40, ), # Params for collect initial_collect_steps=1, collect_episodes_per_iteration=1, replay_buffer_capacity=100000, ou_stddev=0.2, ou_damping=0.15, # Params for target update target_update_tau=0.05, target_update_period=5, # Params for train train_steps_per_iteration=200, batch_size=64, train_sequence_length=10, actor_learning_rate=1e-4, critic_learning_rate=1e-3, dqda_clipping=None, gamma=0.995, reward_scale_factor=1.0, # Params for eval num_eval_episodes=10, eval_interval=1000, # Params for checkpoints, summaries, and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=10000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, eval_metrics_callback=None): """A simple train and eval for DDPG.""" root_dir = os.path.expanduser(root_dir) train_dir = os.path.join(root_dir, 'train') eval_dir = os.path.join(root_dir, 'eval') train_summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=summaries_flush_secs * 1000) train_summary_writer.set_as_default() eval_summary_writer = tf.contrib.summary.create_file_writer( eval_dir, flush_millis=summaries_flush_secs * 1000) eval_metrics = [ py_metrics.AverageReturnMetric(buffer_size=num_eval_episodes), py_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes), ] # TODO(kbanoop): Figure out if it is possible to avoid the with block. with tf.contrib.summary.record_summaries_every_n_global_steps( summary_interval): if observations_whitelist is not None: env_wrappers = [ functools.partial( wrappers.FlattenObservationsWrapper, observations_whitelist=[observations_whitelist]) ] else: env_wrappers = [] environment = suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers) tf_env = tf_py_environment.TFPyEnvironment(environment) eval_py_env = suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers) actor_net = actor_rnn_network.ActorRnnNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), input_fc_layer_params=actor_fc_layers, lstm_size=actor_lstm_size, output_fc_layer_params=actor_output_fc_layers) critic_net = critic_rnn_network.CriticRnnNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), observation_fc_layer_params=critic_obs_fc_layers, action_fc_layer_params=critic_action_fc_layers, joint_fc_layer_params=critic_joint_fc_layers, lstm_size=critic_lstm_size, output_fc_layer_params=critic_output_fc_layers, ) tf_agent = td3_agent.Td3Agent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, critic_network=critic_net, actor_optimizer=tf.train.AdamOptimizer( learning_rate=actor_learning_rate), critic_optimizer=tf.train.AdamOptimizer( learning_rate=critic_learning_rate), ou_stddev=ou_stddev, ou_damping=ou_damping, target_update_tau=target_update_tau, target_update_period=target_update_period, dqda_clipping=dqda_clipping, gamma=gamma, reward_scale_factor=reward_scale_factor, debug_summaries=debug_summaries) replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec(), batch_size=tf_env.batch_size, max_length=replay_buffer_capacity) eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy()) train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_metrics.AverageReturnMetric(), tf_metrics.AverageEpisodeLengthMetric(), ] global_step = tf.train.get_or_create_global_step() # TODO(oars): Refactor drivers to better handle policy states. Remove the # policy reset and passing down an empyt policy state to the driver. collect_policy = tf_agent.collect_policy() policy_state = collect_policy.get_initial_state(tf_env.batch_size) initial_collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch], num_episodes=initial_collect_steps).run(policy_state=policy_state) policy_state = collect_policy.get_initial_state(tf_env.batch_size) collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=collect_episodes_per_iteration).run( policy_state=policy_state) # Need extra step to generate transitions of train_sequence_length. # Dataset generates trajectories with shape [BxTx...] dataset = replay_buffer.as_dataset(num_parallel_calls=3, sample_batch_size=batch_size, num_steps=train_sequence_length + 1).prefetch(3) iterator = dataset.make_initializable_iterator() trajectories, unused_info = iterator.get_next() train_op = tf_agent.train(experience=trajectories, train_step_counter=global_step) train_checkpointer = common_utils.Checkpointer( ckpt_dir=train_dir, agent=tf_agent, global_step=global_step, metrics=tf.contrib.checkpoint.List(train_metrics)) policy_checkpointer = common_utils.Checkpointer( ckpt_dir=os.path.join(train_dir, 'policy'), policy=tf_agent.policy(), global_step=global_step) rb_checkpointer = common_utils.Checkpointer( ckpt_dir=os.path.join(train_dir, 'replay_buffer'), max_to_keep=1, replay_buffer=replay_buffer) for train_metric in train_metrics: train_metric.tf_summaries(step_metrics=train_metrics[:2]) summary_op = tf.contrib.summary.all_summary_ops() with eval_summary_writer.as_default(), \ tf.contrib.summary.always_record_summaries(): for eval_metric in eval_metrics: eval_metric.tf_summaries() init_agent_op = tf_agent.initialize() with tf.Session() as sess: # Initialize the graph. train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) sess.run(iterator.initializer) # TODO(sguada) Remove once Periodically can be saved. common_utils.initialize_uninitialized_variables(sess) sess.run(init_agent_op) tf.contrib.summary.initialize(session=sess) sess.run(initial_collect_op) global_step_val = sess.run(global_step) metric_utils.compute_summaries( eval_metrics, eval_py_env, eval_py_policy, num_episodes=num_eval_episodes, global_step=global_step_val, callback=eval_metrics_callback, ) collect_call = sess.make_callable(collect_op) train_step_call = sess.make_callable( [train_op, summary_op, global_step]) timed_at_step = sess.run(global_step) time_acc = 0 steps_per_second_ph = tf.placeholder(tf.float32, shape=(), name='steps_per_sec_ph') steps_per_second_summary = tf.contrib.summary.scalar( name='global_steps/sec', tensor=steps_per_second_ph) for _ in range(num_iterations): start_time = time.time() collect_call() for _ in range(train_steps_per_iteration): loss_info_value, _, global_step_val = train_step_call() time_acc += time.time() - start_time if global_step_val % log_interval == 0: tf.logging.info('step = %d, loss = %f', global_step_val, loss_info_value.loss) steps_per_sec = (global_step_val - timed_at_step) / time_acc tf.logging.info('%.3f steps/sec' % steps_per_sec) sess.run(steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) timed_at_step = global_step_val time_acc = 0 if global_step_val % train_checkpoint_interval == 0: train_checkpointer.save(global_step=global_step_val) if global_step_val % policy_checkpoint_interval == 0: policy_checkpointer.save(global_step=global_step_val) if global_step_val % rb_checkpoint_interval == 0: rb_checkpointer.save(global_step=global_step_val) if global_step_val % eval_interval == 0: metric_utils.compute_summaries( eval_metrics, eval_py_env, eval_py_policy, num_episodes=num_eval_episodes, global_step=global_step_val, callback=eval_metrics_callback, )
def train_eval( root_dir, env_name='cartpole', task_name='balance', observations_whitelist='position', num_iterations=100000, actor_fc_layers=(400, 300), actor_output_fc_layers=(100, ), actor_lstm_size=(40, ), critic_obs_fc_layers=(400, ), critic_action_fc_layers=None, critic_joint_fc_layers=(300, ), critic_output_fc_layers=(100, ), critic_lstm_size=(40, ), # Params for collect initial_collect_steps=1, collect_episodes_per_iteration=1, replay_buffer_capacity=100000, exploration_noise_std=0.1, # Params for target update target_update_tau=0.05, target_update_period=5, # Params for train train_steps_per_iteration=200, batch_size=64, actor_update_period=2, train_sequence_length=10, actor_learning_rate=1e-4, critic_learning_rate=1e-3, dqda_clipping=None, gamma=0.995, reward_scale_factor=1.0, # Params for eval num_eval_episodes=10, eval_interval=1000, # Params for checkpoints, summaries, and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=10000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, eval_metrics_callback=None): """A simple train and eval for DDPG.""" root_dir = os.path.expanduser(root_dir) train_dir = os.path.join(root_dir, 'train') eval_dir = os.path.join(root_dir, 'eval') train_summary_writer = tf.compat.v2.summary.create_file_writer( train_dir, flush_millis=summaries_flush_secs * 1000) train_summary_writer.set_as_default() eval_summary_writer = tf.compat.v2.summary.create_file_writer( eval_dir, flush_millis=summaries_flush_secs * 1000) eval_metrics = [ py_metrics.AverageReturnMetric(buffer_size=num_eval_episodes), py_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes), ] with tf.compat.v2.summary.record_if( lambda: tf.math.equal(global_step % summary_interval, 0)): if observations_whitelist is not None: env_wrappers = [ functools.partial( wrappers.FlattenObservationsWrapper, observations_whitelist=[observations_whitelist]) ] else: env_wrappers = [] environment = suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers) tf_env = tf_py_environment.TFPyEnvironment(environment) eval_py_env = suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers) actor_net = actor_rnn_network.ActorRnnNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), input_fc_layer_params=actor_fc_layers, lstm_size=actor_lstm_size, output_fc_layer_params=actor_output_fc_layers) critic_net_input_specs = (tf_env.time_step_spec().observation, tf_env.action_spec()) critic_net = critic_rnn_network.CriticRnnNetwork( critic_net_input_specs, observation_fc_layer_params=critic_obs_fc_layers, action_fc_layer_params=critic_action_fc_layers, joint_fc_layer_params=critic_joint_fc_layers, lstm_size=critic_lstm_size, output_fc_layer_params=critic_output_fc_layers, ) global_step = tf.compat.v1.train.get_or_create_global_step() tf_agent = td3_agent.Td3Agent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, critic_network=critic_net, actor_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=actor_learning_rate), critic_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=critic_learning_rate), exploration_noise_std=exploration_noise_std, target_update_tau=target_update_tau, target_update_period=target_update_period, actor_update_period=actor_update_period, dqda_clipping=dqda_clipping, gamma=gamma, reward_scale_factor=reward_scale_factor, debug_summaries=debug_summaries, train_step_counter=global_step) replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec, batch_size=tf_env.batch_size, max_length=replay_buffer_capacity) eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy) train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_metrics.AverageReturnMetric(), tf_metrics.AverageEpisodeLengthMetric(), ] collect_policy = tf_agent.collect_policy policy_state = collect_policy.get_initial_state(tf_env.batch_size) initial_collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=initial_collect_steps).run(policy_state=policy_state) policy_state = collect_policy.get_initial_state(tf_env.batch_size) collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=collect_episodes_per_iteration).run( policy_state=policy_state) # Need extra step to generate transitions of train_sequence_length. # Dataset generates trajectories with shape [BxTx...] dataset = replay_buffer.as_dataset(num_parallel_calls=3, sample_batch_size=batch_size, num_steps=train_sequence_length + 1).prefetch(3) iterator = tf.compat.v1.data.make_initializable_iterator(dataset) trajectories, unused_info = iterator.get_next() train_fn = common.function(tf_agent.train) train_op = train_fn(experience=trajectories) train_checkpointer = common.Checkpointer( ckpt_dir=train_dir, agent=tf_agent, global_step=global_step, metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics')) policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join( train_dir, 'policy'), policy=tf_agent.policy, global_step=global_step) rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join( train_dir, 'replay_buffer'), max_to_keep=1, replay_buffer=replay_buffer) summary_ops = [] for train_metric in train_metrics: summary_ops.append( train_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2])) with eval_summary_writer.as_default(), \ tf.compat.v2.summary.record_if(True): for eval_metric in eval_metrics: eval_metric.tf_summaries(train_step=global_step) init_agent_op = tf_agent.initialize() with tf.compat.v1.Session() as sess: # Initialize the graph. train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) sess.run(iterator.initializer) sess.run(init_agent_op) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) sess.run(initial_collect_op) global_step_val = sess.run(global_step) metric_utils.compute_summaries( eval_metrics, eval_py_env, eval_py_policy, num_episodes=num_eval_episodes, global_step=global_step_val, callback=eval_metrics_callback, log=True, ) collect_call = sess.make_callable(collect_op) train_step_call = sess.make_callable([train_op, summary_ops]) global_step_call = sess.make_callable(global_step) timed_at_step = global_step_call() time_acc = 0 steps_per_second_ph = tf.compat.v1.placeholder( tf.float32, shape=(), name='steps_per_sec_ph') steps_per_second_summary = tf.compat.v2.summary.scalar( name='global_steps_per_sec', data=steps_per_second_ph, step=global_step) for _ in range(num_iterations): start_time = time.time() collect_call() for _ in range(train_steps_per_iteration): loss_info_value, _ = train_step_call() time_acc += time.time() - start_time global_step_val = global_step_call() if global_step_val % log_interval == 0: logging.info('step = %d, loss = %f', global_step_val, loss_info_value.loss) steps_per_sec = (global_step_val - timed_at_step) / time_acc logging.info('%.3f steps/sec', steps_per_sec) sess.run(steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) timed_at_step = global_step_val time_acc = 0 if global_step_val % train_checkpoint_interval == 0: train_checkpointer.save(global_step=global_step_val) if global_step_val % policy_checkpoint_interval == 0: policy_checkpointer.save(global_step=global_step_val) if global_step_val % rb_checkpoint_interval == 0: rb_checkpointer.save(global_step=global_step_val) if global_step_val % eval_interval == 0: metric_utils.compute_summaries( eval_metrics, eval_py_env, eval_py_policy, num_episodes=num_eval_episodes, global_step=global_step_val, callback=eval_metrics_callback, log=True, )
def train_eval( root_dir, env_name='cartpole', task_name='balance', observations_whitelist='position', num_iterations=100000, actor_fc_layers=(400, 300), actor_output_fc_layers=(100, ), actor_lstm_size=(40, ), critic_obs_fc_layers=(400, ), critic_action_fc_layers=None, critic_joint_fc_layers=(300, ), critic_output_fc_layers=(100, ), critic_lstm_size=(40, ), # Params for collect initial_collect_episodes=1, collect_episodes_per_iteration=1, replay_buffer_capacity=100000, ou_stddev=0.2, ou_damping=0.15, # Params for target update target_update_tau=0.05, target_update_period=5, # Params for train # Params for train train_steps_per_iteration=200, batch_size=64, train_sequence_length=10, actor_learning_rate=1e-4, critic_learning_rate=1e-3, dqda_clipping=None, td_errors_loss_fn=None, gamma=0.995, reward_scale_factor=1.0, gradient_clipping=None, use_tf_functions=True, # Params for eval num_eval_episodes=10, eval_interval=1000, # Params for checkpoints, summaries, and logging log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=True, summarize_grads_and_vars=True, eval_metrics_callback=None): """A simple train and eval for DDPG.""" root_dir = os.path.expanduser(root_dir) train_dir = os.path.join(root_dir, 'train') eval_dir = os.path.join(root_dir, 'eval') train_summary_writer = tf.compat.v2.summary.create_file_writer( train_dir, flush_millis=summaries_flush_secs * 1000) train_summary_writer.set_as_default() eval_summary_writer = tf.compat.v2.summary.create_file_writer( eval_dir, flush_millis=summaries_flush_secs * 1000) eval_metrics = [ tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes), tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes) ] global_step = tf.compat.v1.train.get_or_create_global_step() with tf.compat.v2.summary.record_if( lambda: tf.math.equal(global_step % summary_interval, 0)): if observations_whitelist is not None: env_wrappers = [ functools.partial( wrappers.FlattenObservationsWrapper, observations_whitelist=[observations_whitelist]) ] else: env_wrappers = [] tf_env = tf_py_environment.TFPyEnvironment( suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers)) eval_tf_env = tf_py_environment.TFPyEnvironment( suite_dm_control.load(env_name, task_name, env_wrappers=env_wrappers)) actor_net = actor_rnn_network.ActorRnnNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), input_fc_layer_params=actor_fc_layers, lstm_size=actor_lstm_size, output_fc_layer_params=actor_output_fc_layers) critic_net_input_specs = (tf_env.time_step_spec().observation, tf_env.action_spec()) critic_net = critic_rnn_network.CriticRnnNetwork( critic_net_input_specs, observation_fc_layer_params=critic_obs_fc_layers, action_fc_layer_params=critic_action_fc_layers, joint_fc_layer_params=critic_joint_fc_layers, lstm_size=critic_lstm_size, output_fc_layer_params=critic_output_fc_layers, ) tf_agent = ddpg_agent.DdpgAgent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, critic_network=critic_net, actor_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=actor_learning_rate), critic_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=critic_learning_rate), ou_stddev=ou_stddev, ou_damping=ou_damping, target_update_tau=target_update_tau, target_update_period=target_update_period, dqda_clipping=dqda_clipping, td_errors_loss_fn=td_errors_loss_fn, gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars) tf_agent.initialize() train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_metrics.AverageReturnMetric(), tf_metrics.AverageEpisodeLengthMetric(), ] eval_policy = tf_agent.policy collect_policy = tf_agent.collect_policy replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec, batch_size=tf_env.batch_size, max_length=replay_buffer_capacity) initial_collect_driver = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=initial_collect_episodes) collect_driver = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=collect_episodes_per_iteration) if use_tf_functions: initial_collect_driver.run = common.function( initial_collect_driver.run) collect_driver.run = common.function(collect_driver.run) tf_agent.train = common.function(tf_agent.train) # Collect initial replay data. logging.info( 'Initializing replay buffer by collecting experience for %d episodes ' 'with a random policy.', initial_collect_episodes) initial_collect_driver.run() results = metric_utils.eager_compute( eval_metrics, eval_tf_env, eval_policy, num_episodes=num_eval_episodes, train_step=global_step, summary_writer=eval_summary_writer, summary_prefix='Metrics', ) if eval_metrics_callback is not None: eval_metrics_callback(results, global_step.numpy()) metric_utils.log_metrics(eval_metrics) time_step = None policy_state = collect_policy.get_initial_state(tf_env.batch_size) timed_at_step = global_step.numpy() time_acc = 0 # Dataset generates trajectories with shape [BxTx...] dataset = replay_buffer.as_dataset(num_parallel_calls=3, sample_batch_size=batch_size, num_steps=train_sequence_length + 1).prefetch(3) iterator = iter(dataset) for _ in range(num_iterations): start_time = time.time() time_step, policy_state = collect_driver.run( time_step=time_step, policy_state=policy_state, ) for _ in range(train_steps_per_iteration): experience, _ = next(iterator) train_loss = tf_agent.train(experience, train_step_counter=global_step) time_acc += time.time() - start_time if global_step.numpy() % log_interval == 0: logging.info('step = %d, loss = %f', global_step.numpy(), train_loss.loss) steps_per_sec = (global_step.numpy() - timed_at_step) / time_acc logging.info('%.3f steps/sec', steps_per_sec) tf.compat.v2.summary.scalar(name='global_steps_per_sec', data=steps_per_sec, step=global_step) timed_at_step = global_step.numpy() time_acc = 0 for train_metric in train_metrics: train_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2]) if global_step.numpy() % eval_interval == 0: results = metric_utils.eager_compute( eval_metrics, eval_tf_env, eval_policy, num_episodes=num_eval_episodes, train_step=global_step, summary_writer=eval_summary_writer, summary_prefix='Metrics', ) if eval_metrics_callback is not None: eval_metrics_callback(results, global_step.numpy()) metric_utils.log_metrics(eval_metrics) return train_loss
def train_eval(): """A simple train and eval for DDPG.""" logdir = FLAGS.logdir train_dir = os.path.join(logdir, 'train') eval_dir = os.path.join(logdir, 'eval') summary_flush_millis = 10 * 1000 train_summary_writer = tf.compat.v2.summary.create_file_writer( train_dir, flush_millis=summary_flush_millis) train_summary_writer.set_as_default() eval_summary_writer = tf.compat.v2.summary.create_file_writer( eval_dir, flush_millis=summary_flush_millis) eval_metrics = [ tf_metrics.AverageReturnMetric(buffer_size=FLAGS.num_eval_episodes), tf_metrics.AverageEpisodeLengthMetric( buffer_size=FLAGS.num_eval_episodes) ] global_step = tf.compat.v1.train.get_or_create_global_step() with tf.compat.v2.summary.record_if( lambda: tf.math.equal(global_step % 1000, 0)): env = FLAGS.environment tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env)) eval_tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env)) if FLAGS.use_rnn: actor_net = actor_rnn_network.ActorRnnNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), input_fc_layer_params=parse_str_flag(FLAGS.actor_fc_layers), lstm_size=parse_str_flag(FLAGS.actor_lstm_sizes), output_fc_layer_params=FLAGS.actor_output_fc_layers) critic_net = critic_rnn_network.CriticRnnNetwork( input_tensor_spec=(tf_env.time_step_spec().observation, tf_env.action_spec()), observation_fc_layer_params=parse_str_flag( FLAGS.critic_obs_fc_layers), action_fc_layer_params=parse_str_flag( FLAGS.critic_action_fc_layers), joint_fc_layer_params=parse_str_flag( FLAGS.critic_joint_fc_layers), lstm_size=parse_str_flag(FLAGS.critic_lstm_sizes), output_fc_layer_params=parse_str_flag( FLAGS.critic_output_fc_layers)) else: actor_net = actor_network.ActorNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), fc_layer_params=parse_str_flag(FLAGS.actor_fc_layers)) critic_net = critic_network.CriticNetwork( input_tensor_spec=(tf_env.time_step_spec().observation, tf_env.action_spec()), observation_fc_layer_params=parse_str_flag( FLAGS.critic_obs_fc_layers), action_fc_layer_params=parse_str_flag( FLAGS.critic_action_fc_layers), joint_fc_layer_params=parse_str_flag( FLAGS.critic_joint_fc_layers)) tf_agent = td3_agent.Td3Agent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, critic_network=critic_net, actor_optimizer=tf.train.AdamOptimizer( learning_rate=FLAGS.actor_learning_rate), critic_optimizer=tf.train.AdamOptimizer( learning_rate=FLAGS.critic_learning_rate), exploration_noise_std=FLAGS.exploration_noise_std, target_update_tau=FLAGS.target_update_tau, target_update_period=FLAGS.target_update_period, td_errors_loss_fn=None, #tf.compat.v1.losses.huber_loss, gamma=FLAGS.gamma, train_step_counter=global_step) tf_agent.initialize() train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_metrics.AverageReturnMetric(), tf_metrics.AverageEpisodeLengthMetric(), ] eval_policy = tf_agent.policy collect_policy = tf_agent.collect_policy replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec, batch_size=tf_env.batch_size, max_length=FLAGS.replay_buffer_size) initial_collect_driver = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch], num_steps=FLAGS.initial_collect_steps) collect_driver = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_steps=FLAGS.collect_steps_per_iteration) initial_collect_driver.run = common.function( initial_collect_driver.run) collect_driver.run = common.function(collect_driver.run) tf_agent.train = common.function(tf_agent.train) # Collect initial replay data. initial_collect_driver.run() results = metric_utils.eager_compute( eval_metrics, eval_tf_env, eval_policy, num_episodes=FLAGS.num_eval_episodes, train_step=global_step, summary_writer=eval_summary_writer, summary_prefix='Metrics', ) metric_utils.log_metrics(eval_metrics) time_step = None policy_state = collect_policy.get_initial_state(tf_env.batch_size) timed_at_step = global_step.numpy() time_acc = 0 # Dataset to generate trajectories. dataset = replay_buffer.as_dataset( num_parallel_calls=3, sample_batch_size=FLAGS.batch_size, num_steps=(FLAGS.train_sequence_length + 1)).prefetch(3) iterator = iter(dataset) def train_step(): experience, _ = next(iterator) return tf_agent.train(experience) train_step = common.function(train_step) for _ in range(FLAGS.num_iterations): start_time = time.time() time_step, policy_state = collect_driver.run( time_step=time_step, policy_state=policy_state, ) for _ in range(FLAGS.train_steps_per_iteration): train_loss = train_step() time_acc += time.time() - start_time if global_step.numpy() % 1000 == 0: logging.info('step = %d, loss = %f', global_step.numpy(), train_loss.loss) steps_per_sec = (global_step.numpy() - timed_at_step) / time_acc logging.info('%.3f steps/sec', steps_per_sec) tf.compat.v2.summary.scalar(name='global_steps_per_sec', data=steps_per_sec, step=global_step) timed_at_step = global_step.numpy() time_acc = 0 for train_metric in train_metrics: train_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2]) if global_step.numpy() % 10000 == 0: results = metric_utils.eager_compute( eval_metrics, eval_tf_env, eval_policy, num_episodes=FLAGS.num_eval_episodes, train_step=global_step, summary_writer=eval_summary_writer, summary_prefix='Metrics', ) metric_utils.log_metrics(eval_metrics) return train_loss