def testSaveWrappedPolicyRestoreOuterCheckAssertConsumed(self, batch_size=5): actor_policy_save_path = os.path.join(self.get_temp_dir(), 'actor_policy', str(batch_size)) noise_policy_save_path = os.path.join(self.get_temp_dir(), 'noise_policy', str(batch_size)) # Construct a policy to be saved under a tf.Graph instance. policy_saved_graph = tf.Graph() with policy_saved_graph.as_default(): actor_network = DummyActionNet(self._obs_spec, self._float_action_spec) wrapped_policy = actor_policy.ActorPolicy( time_step_spec=self._time_step_spec, action_spec=self._float_action_spec, actor_network=actor_network, clip=False) tf_policy = ou_noise_policy.OUNoisePolicy(wrapped_policy) # Save the exploration policy and the wrapped actor policy. actor_policy_saved = py_tf_policy.PyTFPolicy(wrapped_policy) noise_policy_saved = py_tf_policy.PyTFPolicy(tf_policy) for policy_saved, policy_save_path in zip( [actor_policy_saved, noise_policy_saved], [actor_policy_save_path, noise_policy_save_path]): policy_saved.session = tf.compat.v1.Session(graph=policy_saved_graph) policy_saved.initialize(batch_size) policy_saved.save(policy_dir=policy_save_path, graph=policy_saved_graph) # Construct a policy to be restored under another tf.Graph instance. policy_restore_graph = tf.Graph() with policy_restore_graph.as_default(): actor_network = DummyActionNet(self._obs_spec, self._float_action_spec) wrapped_policy = actor_policy.ActorPolicy( time_step_spec=self._time_step_spec, action_spec=self._float_action_spec, actor_network=actor_network, clip=False) tf_policy = ou_noise_policy.OUNoisePolicy(wrapped_policy) policy_restored = py_tf_policy.PyTFPolicy(tf_policy) policy_restored.session = tf.compat.v1.Session(graph=policy_restore_graph) policy_restored.initialize(batch_size) # 1). Restoring the same noise policy as was saved. policy_restored.restore( policy_dir=noise_policy_save_path, graph=policy_restore_graph) # 2). Restoring the actor policy inside of the noise policy. While the # graph for policy restore contains additional local variable for the # OUNoise, if there is no checking that checkpoint was consumed, this # also works. policy_restored.restore( policy_dir=actor_policy_save_path, graph=policy_restore_graph, assert_consumed=False) # 3). Restoring the actor policy while checking that all variables in # the checkpoint were found in the graph should fail. with self.assertRaisesRegexp( AssertionError, 'Some Python objects were not bound to checkpointed values*'): policy_restored.restore( policy_dir=actor_policy_save_path, graph=policy_restore_graph)
def testSaveRestore(self, batch_size): policy_save_path = os.path.join(tf.flags.FLAGS.test_tmpdir, 'policy', str(batch_size)) # Construct a policy to be saved under a tf.Graph instance. policy_saved_graph = tf.Graph() with policy_saved_graph.as_default(): tf_policy = q_policy.QPolicy( self._time_step_spec, self._action_spec, DummyNet(use_constant_initializer=False)) # Parameterized tests reuse temp directories, check it doesn't exist. with self.assertRaises(tf.errors.NotFoundError): tf.io.gfile.listdir(policy_save_path) policy_saved = py_tf_policy.PyTFPolicy(tf_policy) policy_saved.session = tf.compat.v1.Session( graph=policy_saved_graph) policy_saved.initialize(batch_size) policy_saved.save(policy_dir=policy_save_path, graph=policy_saved_graph) self.assertEqual( set(tf.io.gfile.listdir(policy_save_path)), set([ 'checkpoint', 'ckpt-0.data-00000-of-00001', 'ckpt-0.index' ])) # Construct a policy to be restored under another tf.Graph instance. policy_restore_graph = tf.Graph() with policy_restore_graph.as_default(): tf_policy = q_policy.QPolicy( self._time_step_spec, self._action_spec, DummyNet(use_constant_initializer=False)) policy_restored = py_tf_policy.PyTFPolicy(tf_policy) policy_restored.session = tf.compat.v1.Session( graph=policy_restore_graph) policy_restored.initialize(batch_size) random_init_vals = policy_restored.session.run( tf_policy.variables()) policy_restored.restore(policy_dir=policy_save_path, graph=policy_restore_graph) restored_vals = policy_restored.session.run(tf_policy.variables()) for random_init_var, restored_var in zip(random_init_vals, restored_vals): self.assertFalse(np.array_equal(random_init_var, restored_var)) # Check that variables in the two policies have identical values. with policy_restore_graph.as_default(): restored_values = policy_restored.session.run( tf.compat.v1.global_variables()) with policy_saved_graph.as_default(): initial_values = policy_saved.session.run( tf.compat.v1.global_variables()) # Networks have two fully connected layers. self.assertLen(initial_values, 4) self.assertLen(restored_values, 4) for initial_var, restored_var in zip(initial_values, restored_values): np.testing.assert_array_equal(initial_var, restored_var)
def testSaveRestore(self, batch_size): policy_save_path = os.path.join(flags.FLAGS.test_tmpdir, 'policy', str(batch_size)) # Construct a policy to be saved under a tf.Graph instance. policy_saved_graph = tf.Graph() with policy_saved_graph.as_default(): tf_policy = q_policy.QPolicy(self._time_step_spec, self._action_spec, DummyNet(use_constant_initializer=False)) # Parameterized tests reuse temp directories, make no save exists. try: tf.io.gfile.listdir(policy_save_path) tf.io.gfile.rmtree(policy_save_path) except tf.errors.NotFoundError: pass policy_saved = py_tf_policy.PyTFPolicy(tf_policy) policy_saved.session = tf.compat.v1.Session(graph=policy_saved_graph) policy_saved.initialize(batch_size) policy_saved.save(policy_dir=policy_save_path, graph=policy_saved_graph) # Verify that index files were written. There will also be some number of # data files, but this depends on the number of devices. self.assertContainsSubset( set(['checkpoint', 'ckpt-0.index']), set(tf.io.gfile.listdir(policy_save_path))) # Construct a policy to be restored under another tf.Graph instance. policy_restore_graph = tf.Graph() with policy_restore_graph.as_default(): tf_policy = q_policy.QPolicy(self._time_step_spec, self._action_spec, DummyNet(use_constant_initializer=False)) policy_restored = py_tf_policy.PyTFPolicy(tf_policy) policy_restored.session = tf.compat.v1.Session(graph=policy_restore_graph) policy_restored.initialize(batch_size) random_init_vals = policy_restored.session.run(tf_policy.variables()) policy_restored.restore( policy_dir=policy_save_path, graph=policy_restore_graph) restored_vals = policy_restored.session.run(tf_policy.variables()) for random_init_var, restored_var in zip(random_init_vals, restored_vals): self.assertFalse(np.array_equal(random_init_var, restored_var)) # Check that variables in the two policies have identical values. with policy_restore_graph.as_default(): restored_values = policy_restored.session.run( tf.compat.v1.global_variables()) with policy_saved_graph.as_default(): initial_values = policy_saved.session.run(tf.compat.v1.global_variables()) # Networks have two fully connected layers. self.assertLen(initial_values, 4) self.assertLen(restored_values, 4) for initial_var, restored_var in zip(initial_values, restored_values): np.testing.assert_array_equal(initial_var, restored_var)
def __init__(self, train_dir): # --- Tf session --- # tf.reset_default_graph() self.sess = tf.Session() tf.compat.v1.enable_resource_variables() # --- Environment Stub--- # env = rl_env.make(environment_name=FLAGS.game_type, num_players=FLAGS.num_players) wrapped_env = PyhanabiEnvWrapper(env) tf_env = tf_py_environment.TFPyEnvironment(wrapped_env) with self.sess.as_default(): # --- Init Networks --- # actor_net, value_net = load_networks(tf_env) # --- Init agent --- # agent = PPOAgent( # set up ppo agent with tf_agents tf_env.time_step_spec(), tf_env.action_spec(), actor_net=actor_net, value_net=value_net, train_step_counter=tf.compat.v1.train. get_or_create_global_step(), normalize_observations=False) # --- init policy --- # self.policy = py_tf_policy.PyTFPolicy(agent.policy) self.policy.initialize(None) # --- restore from checkpoint --- # self.policy.restore(policy_dir=train_dir, assert_consumed=False) # Run tf graph self.sess.run(agent.initialize())
def testAssignSession(self, batch_size): policy = py_tf_policy.PyTFPolicy(self._tf_policy) policy.session = tf.Session() expected_initial_state = np.zeros([batch_size or 1, 1], dtype=np.float32) self.assertTrue( np.array_equal( policy.get_initial_state(batch_size), expected_initial_state))
def testZeroState(self, batch_size): policy = py_tf_policy.PyTFPolicy(self._tf_policy) expected_initial_state = np.zeros([batch_size or 1, 1], dtype=np.float32) with self.test_session(): self.assertTrue( np.array_equal( policy.get_initial_state(batch_size), expected_initial_state))
def testAction(self, batch_size): if tf.executing_eagerly(): self.skipTest('b/123770140: Handling sessions with eager mode is buggy') single_observation = np.array([1, 2], dtype=np.float32) time_steps = ts.restart(single_observation) if batch_size is not None: time_steps = [time_steps] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) policy = py_tf_policy.PyTFPolicy(self._tf_policy) with self.cached_session(): policy_state = policy.get_initial_state(batch_size) self.evaluate(tf.compat.v1.global_variables_initializer()) action_steps = policy.action(time_steps, policy_state) self.assertEqual(action_steps.action.dtype, np.int32) if batch_size is None: self.assertEqual(action_steps.action.shape, ()) self.assertIn(action_steps.action, (0, 1)) self.assertEqual(action_steps.state, np.zeros([1, 1])) else: self.assertEqual(action_steps.action.shape, (batch_size,)) for a in action_steps.action: self.assertIn(a, (0, 1)) self.assertAllEqual(action_steps.state, np.zeros([batch_size, 1]))
def testRaiseValueErrorWithoutSession(self): policy = py_tf_policy.PyTFPolicy(self._tf_policy) with self.assertRaisesRegexp( AttributeError, "No TensorFlow session-like object was set on this 'PyTFPolicy'.*" ): policy.get_initial_state()
def testDeferredBatchingAction(self): if tf.executing_eagerly(): self.skipTest('b/123770140: Handling sessions with eager mode is buggy') # Construct policy without providing batch_size. tf_policy = q_policy.QPolicy( self._time_step_spec, self._action_spec, q_network=DummyNet(stateful=False)) policy = py_tf_policy.PyTFPolicy(tf_policy) # But time_steps have batch_size of 5 batch_size = 5 single_observation = np.array([1, 2], dtype=np.float32) time_steps = [ts.restart(single_observation)] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) with self.cached_session(): self.evaluate(tf.compat.v1.global_variables_initializer()) action_steps = policy.action(time_steps) self.assertEqual(action_steps.action.shape, (batch_size,)) for a in action_steps.action: self.assertIn(a, (0, 1)) self.assertAllEqual(action_steps.state, ())
def testBuild(self): policy = py_tf_policy.PyTFPolicy(self._tf_policy) expected_time_step_spec = ts.time_step_spec( tensor_spec.to_nest_array_spec(self._obs_spec)) expected_action_spec = tensor_spec.to_nest_array_spec(self._action_spec) self.assertEqual(expected_time_step_spec, policy.time_step_spec()) self.assertEqual(expected_action_spec, policy.action_spec())
def testRaiseValueErrorWithoutSession(self): if tf.executing_eagerly(): self.skipTest('b/123770140: Handling sessions with eager mode is buggy') policy = py_tf_policy.PyTFPolicy(self._tf_policy) with self.assertRaisesRegexp( AttributeError, "No TensorFlow session-like object was set on this 'PyTFPolicy'.*"): policy.get_initial_state()
def testZeroState(self, batch_size): if tf.executing_eagerly(): self.skipTest('b/123770140: Handling sessions with eager mode is buggy') policy = py_tf_policy.PyTFPolicy(self._tf_policy) expected_initial_state = np.zeros([batch_size or 1, 1], dtype=np.float32) with self.cached_session(): self.assertTrue( np.array_equal( policy.get_initial_state(batch_size), expected_initial_state))
def testAssignSession(self, batch_size): if tf.executing_eagerly(): self.skipTest('b/123770140') policy = py_tf_policy.PyTFPolicy(self._tf_policy) policy.session = tf.compat.v1.Session() expected_initial_state = np.zeros([batch_size or 1, 1], dtype=np.float32) self.assertTrue( np.array_equal( policy.get_initial_state(batch_size), expected_initial_state))
def __init__(self, root_dir, game_type='Hanabi-Full', num_players=4, actor_fc_layers=(100, ), value_fc_layers=(100, ), use_value_network=False ): tf.reset_default_graph() self.sess = tf.Session() tf.compat.v1.enable_resource_variables() pyhanabi_env = rl_env.make(environment_name=game_type, num_players=num_players) py_env = PyhanabiEnvWrapper(pyhanabi_env) tf_env = tf_py_environment.TFPyEnvironment(py_env) with self.sess.as_default(): # init the agent actor_net = masked_networks.MaskedActorDistributionNetwork( tf_env.observation_spec(), tf_env.action_spec(), fc_layer_params=actor_fc_layers ) value_network = None if use_value_network: value_network = MaskedValueNetwork( tf_env.observation_spec(), fc_layer_params=value_fc_layers ) global_step = tf.compat.v1.train.get_or_create_global_step() # necessary ??? => Yes baby tf_agent = reinforce_agent.ReinforceAgent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, value_network=value_network if use_value_network else None, value_estimation_loss_coef=.2, gamma=.9, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3), debug_summaries=False, summarize_grads_and_vars=False, train_step_counter=global_step ) self.policy = py_tf_policy.PyTFPolicy(tf_agent.policy) # load checkpoint #train_dir = os.path.join(root_dir, 'train') self.policy.initialize(None) self.policy.restore(root_dir) init_agent_op = tf_agent.initialize() self.sess.run(init_agent_op)
def testDeferredBatchingStateful(self): # Construct policy without providing batch_size. policy = py_tf_policy.PyTFPolicy(self._tf_policy) # But time_steps have batch_size of 5 batch_size = 5 single_observation = np.array([1, 2], dtype=np.float32) time_steps = [ts.restart(single_observation)] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) with self.test_session(): initial_state = policy.get_initial_state(batch_size=batch_size) self.assertAllEqual(initial_state, np.zeros([5, 1])) action_steps = policy.action(time_steps, initial_state) self.assertEqual(action_steps.action.shape, (batch_size, )) self.assertAllEqual(action_steps.action, [1] * batch_size) self.assertAllEqual(action_steps.state, np.zeros([5, 1]))
def testDeferredBatchingAction(self): # Construct policy without providing batch_size. tf_policy = q_policy.QPolicy(self._time_step_spec, self._action_spec, q_network=DummyNet(stateful=False)) policy = py_tf_policy.PyTFPolicy(tf_policy) # But time_steps have batch_size of 5 batch_size = 5 single_observation = np.array([1, 2], dtype=np.float32) time_steps = [ts.restart(single_observation)] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) with self.test_session(): tf.global_variables_initializer().run() action_steps = policy.action(time_steps) self.assertEqual(action_steps.action.shape, (batch_size, )) self.assertAllEqual(action_steps.action, [1] * batch_size) self.assertAllEqual(action_steps.state, ())
def testAction(self, batch_size): single_observation = np.array([1, 2], dtype=np.float32) time_steps = ts.restart(single_observation) if batch_size is not None: time_steps = [time_steps] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) policy = py_tf_policy.PyTFPolicy(self._tf_policy) with self.test_session(): policy_state = policy.get_initial_state(batch_size) tf.global_variables_initializer().run() action_steps = policy.action(time_steps, policy_state) self.assertEqual(action_steps.action.dtype, np.int32) if batch_size is None: self.assertEqual(action_steps.action.shape, ()) self.assertEqual(action_steps.action, 1) self.assertEqual(action_steps.state, np.zeros([1, 1])) else: self.assertEqual(action_steps.action.shape, (batch_size, )) self.assertAllEqual(action_steps.action, [1] * batch_size) self.assertAllEqual(action_steps.state, np.zeros([5, 1]))
def testDeferredBatchingStateful(self): if tf.executing_eagerly(): self.skipTest('b/123770140: Handling sessions with eager mode is buggy') # Construct policy without providing batch_size. policy = py_tf_policy.PyTFPolicy(self._tf_policy) # But time_steps have batch_size of 5 batch_size = 5 single_observation = np.array([1, 2], dtype=np.float32) time_steps = [ts.restart(single_observation)] * batch_size time_steps = fast_map_structure(lambda *arrays: np.stack(arrays), *time_steps) with self.cached_session(): initial_state = policy.get_initial_state(batch_size=batch_size) self.assertAllEqual(initial_state, np.zeros([5, 1])) action_steps = policy.action(time_steps, initial_state) self.assertEqual(action_steps.action.shape, (batch_size,)) for a in action_steps.action: self.assertIn(a, (0, 1)) self.assertAllEqual(action_steps.state, np.zeros([5, 1]))
def train_eval( root_dir, env_name='HalfCheetah-v2', num_iterations=1000000, actor_fc_layers=(256, 256), critic_obs_fc_layers=None, critic_action_fc_layers=None, critic_joint_fc_layers=(256, 256), # Params for collect initial_collect_steps=10000, collect_steps_per_iteration=1, replay_buffer_capacity=1000000, # Params for target update target_update_tau=0.005, target_update_period=1, # Params for train train_steps_per_iteration=1, batch_size=256, actor_learning_rate=3e-4, critic_learning_rate=3e-4, alpha_learning_rate=3e-4, td_errors_loss_fn=tf.compat.v1.losses.mean_squared_error, gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval num_eval_episodes=30, eval_interval=10000, # Params for summaries and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=50000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for SAC.""" 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), ] eval_summary_flush_op = eval_summary_writer.flush() 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)): # Create the environment. tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name)) eval_py_env = suite_mujoco.load(env_name) # Get the data specs from the environment time_step_spec = tf_env.time_step_spec() observation_spec = time_step_spec.observation action_spec = tf_env.action_spec() actor_net = actor_distribution_network.ActorDistributionNetwork( observation_spec, action_spec, fc_layer_params=actor_fc_layers, continuous_projection_net=normal_projection_net) critic_net = critic_network.CriticNetwork( (observation_spec, 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) tf_agent = sac_agent.SacAgent( time_step_spec, 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), alpha_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=alpha_learning_rate), target_update_tau=target_update_tau, target_update_period=target_update_period, 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, train_step_counter=global_step) # Make the replay buffer. replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( data_spec=tf_agent.collect_data_spec, batch_size=1, max_length=replay_buffer_capacity) replay_observer = [replay_buffer.add_batch] eval_py_policy = py_tf_policy.PyTFPolicy( greedy_policy.GreedyPolicy(tf_agent.policy)) train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_py_metric.TFPyMetric(py_metrics.AverageReturnMetric()), tf_py_metric.TFPyMetric(py_metrics.AverageEpisodeLengthMetric()), ] collect_policy = tf_agent.collect_policy initial_collect_policy = random_tf_policy.RandomTFPolicy( tf_env.time_step_spec(), tf_env.action_spec()) initial_collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, initial_collect_policy, observers=replay_observer + train_metrics, num_steps=initial_collect_steps).run() collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=replay_observer + train_metrics, num_steps=collect_steps_per_iteration).run() # Prepare replay buffer as dataset with invalid transitions filtered. def _filter_invalid_transition(trajectories, unused_arg1): return ~trajectories.is_boundary()[0] dataset = replay_buffer.as_dataset( sample_batch_size=5 * batch_size, num_steps=2).apply(tf.data.experimental.unbatch()).filter( _filter_invalid_transition).batch(batch_size).prefetch( batch_size * 5) dataset_iterator = tf.compat.v1.data.make_initializable_iterator( dataset) trajectories, unused_info = dataset_iterator.get_next() train_op = tf_agent.train(trajectories) 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) 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) with tf.compat.v1.Session() as sess: # Initialize graph. train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) # Initialize training. sess.run(dataset_iterator.initializer) common.initialize_uninitialized_variables(sess) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) global_step_val = sess.run(global_step) if global_step_val == 0: # Initial eval of randomly initialized policy 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, ) sess.run(eval_summary_flush_op) # Run initial collect. logging.info('Global step %d: Running initial collect op.', global_step_val) sess.run(initial_collect_op) # Checkpoint the initial replay buffer contents. rb_checkpointer.save(global_step=global_step_val) logging.info('Finished initial collect.') else: logging.info('Global step %d: Skipping initial collect op.', global_step_val) 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): total_loss, _ = 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, total_loss.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 % 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, ) sess.run(eval_summary_flush_op) 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)
def __init__( self, root_dir, env_name, num_iterations=200, max_episode_frames=108000, # ALE frames terminal_on_life_loss=False, conv_layer_params=((32, (8, 8), 4), (64, (4, 4), 2), (64, (3, 3), 1)), fc_layer_params=(512, ), # Params for collect initial_collect_steps=80000, # ALE frames epsilon_greedy=0.01, epsilon_decay_period=1000000, # ALE frames replay_buffer_capacity=1000000, # Params for train train_steps_per_iteration=1000000, # ALE frames update_period=16, # ALE frames target_update_tau=1.0, target_update_period=32000, # ALE frames batch_size=32, learning_rate=2.5e-4, n_step_update=2, gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval do_eval=True, eval_steps_per_iteration=500000, # ALE frames eval_epsilon_greedy=0.001, # 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 Atari train and eval for DQN. Args: root_dir: Directory to write log files to. env_name: Fully-qualified name of the Atari environment (i.e. Pong-v0). num_iterations: Number of train/eval iterations to run. max_episode_frames: Maximum length of a single episode, in ALE frames. terminal_on_life_loss: Whether to simulate an episode termination when a life is lost. conv_layer_params: Params for convolutional layers of QNetwork. fc_layer_params: Params for fully connected layers of QNetwork. initial_collect_steps: Number of frames to ALE frames to process before beginning to train. Since this is in ALE frames, there will be initial_collect_steps/4 items in the replay buffer when training starts. epsilon_greedy: Final epsilon value to decay to for training. epsilon_decay_period: Period over which to decay epsilon, from 1.0 to epsilon_greedy (defined above). replay_buffer_capacity: Maximum number of items to store in the replay buffer. train_steps_per_iteration: Number of ALE frames to run through for each iteration of training. update_period: Run a train operation every update_period ALE frames. target_update_tau: Coeffecient for soft target network updates (1.0 == hard updates). target_update_period: Period, in ALE frames, to copy the live network to the target network. batch_size: Number of frames to include in each training batch. learning_rate: RMS optimizer learning rate. n_step_update: The number of steps to consider when computing TD error and TD loss. Applies standard single-step updates when set to 1. gamma: Discount for future rewards. reward_scale_factor: Scaling factor for rewards. gradient_clipping: Norm length to clip gradients. do_eval: If True, run an eval every iteration. If False, skip eval. eval_steps_per_iteration: Number of ALE frames to run through for each iteration of evaluation. eval_epsilon_greedy: Epsilon value to use for the evaluation policy (0 == totally greedy policy). log_interval: Log stats to the terminal every log_interval training steps. summary_interval: Write TF summaries every summary_interval training steps. summaries_flush_secs: Flush summaries to disk every summaries_flush_secs seconds. debug_summaries: If True, write additional summaries for debugging (see dqn_agent for which summaries are written). summarize_grads_and_vars: Include gradients in summaries. eval_metrics_callback: A callback function that takes (metric_dict, global_step) as parameters. Called after every eval with the results of the evaluation. """ self._update_period = update_period / ATARI_FRAME_SKIP self._train_steps_per_iteration = (train_steps_per_iteration / ATARI_FRAME_SKIP) self._do_eval = do_eval self._eval_steps_per_iteration = eval_steps_per_iteration / ATARI_FRAME_SKIP self._eval_epsilon_greedy = eval_epsilon_greedy self._initial_collect_steps = initial_collect_steps / ATARI_FRAME_SKIP self._summary_interval = summary_interval self._num_iterations = num_iterations self._log_interval = log_interval self._eval_metrics_callback = eval_metrics_callback with gin.unlock_config(): gin.bind_parameter(('tf_agents.environments.atari_preprocessing.' 'AtariPreprocessing.terminal_on_life_loss'), terminal_on_life_loss) 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() self._train_summary_writer = train_summary_writer self._eval_summary_writer = None if self._do_eval: self._eval_summary_writer = tf.compat.v2.summary.create_file_writer( eval_dir, flush_millis=summaries_flush_secs * 1000) self._eval_metrics = [ py_metrics.AverageReturnMetric(name='PhaseAverageReturn', buffer_size=np.inf), py_metrics.AverageEpisodeLengthMetric( name='PhaseAverageEpisodeLength', buffer_size=np.inf), ] self._global_step = tf.compat.v1.train.get_or_create_global_step() with tf.compat.v2.summary.record_if(lambda: tf.math.equal( self._global_step % self._summary_interval, 0)): self._env = suite_atari.load( env_name, max_episode_steps=max_episode_frames / ATARI_FRAME_SKIP, gym_env_wrappers=suite_atari. DEFAULT_ATARI_GYM_WRAPPERS_WITH_STACKING) self._env = batched_py_environment.BatchedPyEnvironment( [self._env]) observation_spec = tensor_spec.from_spec( self._env.observation_spec()) time_step_spec = ts.time_step_spec(observation_spec) action_spec = tensor_spec.from_spec(self._env.action_spec()) with tf.device('/cpu:0'): epsilon = tf.compat.v1.train.polynomial_decay( 1.0, self._global_step, epsilon_decay_period / ATARI_FRAME_SKIP / self._update_period, end_learning_rate=epsilon_greedy) with tf.device('/gpu:0'): optimizer = tf.compat.v1.train.RMSPropOptimizer( learning_rate=learning_rate, decay=0.95, momentum=0.0, epsilon=0.00001, centered=True) categorical_q_net = AtariCategoricalQNetwork( observation_spec, action_spec, conv_layer_params=conv_layer_params, fc_layer_params=fc_layer_params) agent = categorical_dqn_agent.CategoricalDqnAgent( time_step_spec, action_spec, categorical_q_network=categorical_q_net, optimizer=optimizer, epsilon_greedy=epsilon, n_step_update=n_step_update, target_update_tau=target_update_tau, target_update_period=(target_update_period / ATARI_FRAME_SKIP / self._update_period), gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, train_step_counter=self._global_step) self._collect_policy = py_tf_policy.PyTFPolicy( agent.collect_policy) if self._do_eval: self._eval_policy = py_tf_policy.PyTFPolicy( epsilon_greedy_policy.EpsilonGreedyPolicy( policy=agent.policy, epsilon=self._eval_epsilon_greedy)) py_observation_spec = self._env.observation_spec() py_time_step_spec = ts.time_step_spec(py_observation_spec) py_action_spec = policy_step.PolicyStep( self._env.action_spec()) data_spec = trajectory.from_transition(py_time_step_spec, py_action_spec, py_time_step_spec) self._replay_buffer = py_hashed_replay_buffer.PyHashedReplayBuffer( data_spec=data_spec, capacity=replay_buffer_capacity) with tf.device('/cpu:0'): ds = self._replay_buffer.as_dataset( sample_batch_size=batch_size, num_steps=n_step_update + 1) ds = ds.prefetch(4) ds = ds.apply( tf.data.experimental.prefetch_to_device('/gpu:0')) with tf.device('/gpu:0'): self._ds_itr = tf.compat.v1.data.make_one_shot_iterator(ds) experience = self._ds_itr.get_next() self._train_op = agent.train(experience) self._env_steps_metric = py_metrics.EnvironmentSteps() self._step_metrics = [ py_metrics.NumberOfEpisodes(), self._env_steps_metric, ] self._train_metrics = self._step_metrics + [ py_metrics.AverageReturnMetric(buffer_size=10), py_metrics.AverageEpisodeLengthMetric(buffer_size=10), ] # The _train_phase_metrics average over an entire train iteration, # rather than the rolling average of the last 10 episodes. self._train_phase_metrics = [ py_metrics.AverageReturnMetric(name='PhaseAverageReturn', buffer_size=np.inf), py_metrics.AverageEpisodeLengthMetric( name='PhaseAverageEpisodeLength', buffer_size=np.inf), ] self._iteration_metric = py_metrics.CounterMetric( name='Iteration') # Summaries written from python should run every time they are # generated. with tf.compat.v2.summary.record_if(True): self._steps_per_second_ph = tf.compat.v1.placeholder( tf.float32, shape=(), name='steps_per_sec_ph') self._steps_per_second_summary = tf.compat.v2.summary.scalar( name='global_steps_per_sec', data=self._steps_per_second_ph, step=self._global_step) for metric in self._train_metrics: metric.tf_summaries(train_step=self._global_step, step_metrics=self._step_metrics) for metric in self._train_phase_metrics: metric.tf_summaries( train_step=self._global_step, step_metrics=(self._iteration_metric, )) self._iteration_metric.tf_summaries( train_step=self._global_step) if self._do_eval: with self._eval_summary_writer.as_default(): for metric in self._eval_metrics: metric.tf_summaries( train_step=self._global_step, step_metrics=(self._iteration_metric, )) self._train_checkpointer = common.Checkpointer( ckpt_dir=train_dir, agent=agent, global_step=self._global_step, optimizer=optimizer, metrics=metric_utils.MetricsGroup( self._train_metrics + self._train_phase_metrics + [self._iteration_metric], 'train_metrics')) self._policy_checkpointer = common.Checkpointer( ckpt_dir=os.path.join(train_dir, 'policy'), policy=agent.policy, global_step=self._global_step) self._rb_checkpointer = common.Checkpointer( ckpt_dir=os.path.join(train_dir, 'replay_buffer'), max_to_keep=1, replay_buffer=self._replay_buffer) self._init_agent_op = agent.initialize()
def train_eval( root_dir, env_name='CartPole-v0', num_iterations=100000, fc_layer_params=(100, ), # Params for collect initial_collect_steps=1000, collect_steps_per_iteration=1, epsilon_greedy=0.1, replay_buffer_capacity=100000, # Params for target update target_update_tau=0.05, target_update_period=5, # Params for train train_steps_per_iteration=1, batch_size=64, learning_rate=1e-3, n_step_update=1, gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval num_eval_episodes=10, eval_interval=1000, # Params for checkpoints, summaries and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, log_interval=1000, summaries_flush_secs=10, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for DQN.""" 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), ] # Note this is a python environment. env = batched_py_environment.BatchedPyEnvironment( [suite_gym.load(env_name)]) eval_py_env = suite_gym.load(env_name) # Convert specs to BoundedTensorSpec. action_spec = tensor_spec.from_spec(env.action_spec()) observation_spec = tensor_spec.from_spec(env.observation_spec()) time_step_spec = ts.time_step_spec(observation_spec) q_net = q_network.QNetwork(tensor_spec.from_spec(env.observation_spec()), tensor_spec.from_spec(env.action_spec()), fc_layer_params=fc_layer_params) # The agent must be in graph. global_step = tf.compat.v1.train.get_or_create_global_step() agent = dqn_agent.DqnAgent( time_step_spec, action_spec, q_network=q_net, epsilon_greedy=epsilon_greedy, n_step_update=n_step_update, target_update_tau=target_update_tau, target_update_period=target_update_period, optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=learning_rate), td_errors_loss_fn=dqn_agent.element_wise_squared_loss, gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, train_step_counter=global_step) tf_collect_policy = agent.collect_policy collect_policy = py_tf_policy.PyTFPolicy(tf_collect_policy) greedy_policy = py_tf_policy.PyTFPolicy(agent.policy) random_policy = random_py_policy.RandomPyPolicy(env.time_step_spec(), env.action_spec()) # Python replay buffer. replay_buffer = py_uniform_replay_buffer.PyUniformReplayBuffer( capacity=replay_buffer_capacity, data_spec=tensor_spec.to_nest_array_spec(agent.collect_data_spec)) time_step = env.reset() # Initialize the replay buffer with some transitions. We use the random # policy to initialize the replay buffer to make sure we get a good # distribution of actions. for _ in range(initial_collect_steps): time_step = collect_step(env, time_step, random_policy, replay_buffer) # TODO(b/112041045) Use global_step as counter. train_checkpointer = common.Checkpointer(ckpt_dir=train_dir, agent=agent, global_step=global_step) policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join( train_dir, 'policy'), policy=agent.policy, global_step=global_step) ds = replay_buffer.as_dataset(sample_batch_size=batch_size, num_steps=n_step_update + 1) ds = ds.prefetch(4) itr = tf.compat.v1.data.make_initializable_iterator(ds) experience = itr.get_next() train_op = common.function(agent.train)(experience) 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) with tf.compat.v1.Session() as session: train_checkpointer.initialize_or_restore(session) common.initialize_uninitialized_variables(session) session.run(itr.initializer) # Copy critic network values to the target critic network. session.run(agent.initialize()) train = session.make_callable(train_op) global_step_call = session.make_callable(global_step) session.run(train_summary_writer.init()) session.run(eval_summary_writer.init()) # Compute initial evaluation metrics. global_step_val = global_step_call() metric_utils.compute_summaries( eval_metrics, eval_py_env, greedy_policy, num_episodes=num_eval_episodes, global_step=global_step_val, log=True, callback=eval_metrics_callback, ) timed_at_step = global_step_val collect_time = 0 train_time = 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() for _ in range(collect_steps_per_iteration): time_step = collect_step(env, time_step, collect_policy, replay_buffer) collect_time += time.time() - start_time start_time = time.time() for _ in range(train_steps_per_iteration): loss = train() train_time += 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.loss) steps_per_sec = ((global_step_val - timed_at_step) / (collect_time + train_time)) session.run(steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) logging.info('%.3f steps/sec', steps_per_sec) logging.info( '%s', 'collect_time = {}, train_time = {}'.format( collect_time, train_time)) timed_at_step = global_step_val collect_time = 0 train_time = 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 % eval_interval == 0: metric_utils.compute_summaries( eval_metrics, eval_py_env, greedy_policy, num_episodes=num_eval_episodes, global_step=global_step_val, log=True, callback=eval_metrics_callback, ) # Reset timing to avoid counting eval time. timed_at_step = global_step_val start_time = time.time()
def train_eval( root_dir, env_name='CartPole-v0', num_iterations=100000, fc_layer_params=(100,), # Params for collect initial_collect_steps=1000, collect_steps_per_iteration=1, epsilon_greedy=0.1, replay_buffer_capacity=100000, # Params for target update target_update_tau=0.05, target_update_period=5, # Params for train train_steps_per_iteration=1, batch_size=64, learning_rate=1e-3, gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, # 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=20000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, agent_class=dqn_agent.DqnAgent, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for DQN.""" 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), ] 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)): tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env_name)) eval_py_env = suite_gym.load(env_name) q_net = q_network.QNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), fc_layer_params=fc_layer_params) # TODO(b/127301657): Decay epsilon based on global step, cf. cl/188907839 tf_agent = agent_class( tf_env.time_step_spec(), tf_env.action_spec(), q_network=q_net, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate), epsilon_greedy=epsilon_greedy, target_update_tau=target_update_tau, target_update_period=target_update_period, td_errors_loss_fn=common.element_wise_squared_loss, gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, 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(), ] replay_observer = [replay_buffer.add_batch] initial_collect_policy = random_tf_policy.RandomTFPolicy( tf_env.time_step_spec(), tf_env.action_spec()) initial_collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, initial_collect_policy, observers=replay_observer + train_metrics, num_steps=initial_collect_steps).run() collect_policy = tf_agent.collect_policy collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=replay_observer + train_metrics, num_steps=collect_steps_per_iteration).run() # Dataset generates trajectories with shape [Bx2x...] dataset = replay_buffer.as_dataset( num_parallel_calls=3, sample_batch_size=batch_size, num_steps=2).prefetch(3) iterator = tf.compat.v1.data.make_initializable_iterator(dataset) experience, _ = iterator.get_next() train_op = common.function(tf_agent.train)(experience=experience) 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) common.initialize_uninitialized_variables(sess) 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) global_step_call = sess.make_callable(global_step) train_step_call = sess.make_callable([train_op, summary_ops]) timed_at_step = global_step_call() collect_time = 0 train_time = 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): # Train/collect/eval. start_time = time.time() collect_call() collect_time += time.time() - start_time start_time = time.time() for _ in range(train_steps_per_iteration): loss_info_value, _ = train_step_call() train_time += 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) / (collect_time + train_time)) sess.run( steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) logging.info('%.3f steps/sec', steps_per_sec) logging.info('%s', 'collect_time = {}, train_time = {}'.format( collect_time, train_time)) timed_at_step = global_step_val collect_time = 0 train_time = 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='HalfCheetah-v2', num_iterations=2000000, actor_fc_layers=(400, 300), critic_obs_fc_layers=(400,), critic_action_fc_layers=None, critic_joint_fc_layers=(300,), # Params for collect initial_collect_steps=1000, collect_steps_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=1, batch_size=64, actor_update_period=2, actor_learning_rate=1e-4, critic_learning_rate=1e-3, dqda_clipping=None, td_errors_loss_fn=tf.compat.v1.losses.huber_loss, gamma=0.995, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval num_eval_episodes=10, eval_interval=10000, # Params for checkpoints, summaries, and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=20000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for TD3.""" 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), ] 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)): tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name)) eval_py_env = suite_mujoco.load(env_name) actor_net = actor_network.ActorNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), fc_layer_params=actor_fc_layers, ) critic_net_input_specs = (tf_env.time_step_spec().observation, tf_env.action_spec()) critic_net = critic_network.CriticNetwork( 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, ) 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, 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, 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 initial_collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_steps=initial_collect_steps).run() collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_steps=collect_steps_per_iteration).run() dataset = replay_buffer.as_dataset( num_parallel_calls=3, sample_batch_size=batch_size, num_steps=2).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) # TODO(b/126239733): Remove once Periodically can be saved. common.initialize_uninitialized_variables(sess) 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]) timed_at_step = sess.run(global_step) 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, _, global_step_val = train_step_call() time_acc += time.time() - start_time 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( ############################################## # types of params: # 0: specific to algorithm (gin file 0) # 1: specific to environment (gin file 1) # 2: specific to experiment (gin file 2 + command line) # Note: there are other important params # in eg ModelDistributionNetwork that the gin files specify # like sparse vs dense rewards, latent dimensions, etc. ############################################## # basic params for running/logging experiment root_dir, # 2 experiment_name, # 2 num_iterations=int(1e7), # 2 seed=1, # 2 gpu_allow_growth=False, # 2 gpu_memory_limit=None, # 2 verbose=True, # 2 policy_checkpoint_freq_in_iter=100, # policies needed for future eval # 2 train_checkpoint_freq_in_iter=0, #default don't save # 2 rb_checkpoint_freq_in_iter=0, #default don't save # 2 logging_freq_in_iter=10, # printing to terminal # 2 summary_freq_in_iter=10, # saving to tb # 2 num_images_per_summary=2, # 2 summaries_flush_secs=10, # 2 max_episode_len_override=None, # 2 num_trials_to_render=1, # 2 # environment, action mode, etc. env_name='HalfCheetah-v2', # 1 action_repeat=1, # 1 action_mode='joint_position', # joint_position or joint_delta_position # 1 double_camera=False, # camera input # 1 universe='gym', # default task_reward_dim=1, # default # dims for all networks actor_fc_layers=(256, 256), # 1 critic_obs_fc_layers=None, # 1 critic_action_fc_layers=None, # 1 critic_joint_fc_layers=(256, 256), # 1 num_repeat_when_concatenate=None, # 1 # networks critic_input='state', # 0 actor_input='state', # 0 # specifying tasks and eval episodes_per_trial=1, # 2 num_train_tasks=10, # 2 num_eval_tasks=10, # 2 num_eval_trials=10, # 2 eval_interval=10, # 2 eval_on_holdout_tasks=True, # 2 # data collection/buffer init_collect_trials_per_task=None, # 2 collect_trials_per_task=None, # 2 num_tasks_to_collect_per_iter=5, # 2 replay_buffer_capacity=int(1e5), # 2 # training init_model_train_ratio=0.8, # 2 model_train_ratio=1, # 2 model_train_freq=1, # 2 ac_train_ratio=1, # 2 ac_train_freq=1, # 2 num_tasks_per_train=5, # 2 train_trials_per_task=5, # 2 model_bs_in_steps=256, # 2 ac_bs_in_steps=128, # 2 # default AC learning rates, gamma, etc. target_update_tau=0.005, target_update_period=1, actor_learning_rate=3e-4, critic_learning_rate=3e-4, alpha_learning_rate=3e-4, model_learning_rate=1e-4, td_errors_loss_fn=functools.partial( tf.compat.v1.losses.mean_squared_error, weights=0.5), gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, log_image_strips=False, stop_model_training=1E10, eval_only=False, # evaluate checkpoints ONLY log_image_observations=False, load_offline_data=False, # whether to use offline data offline_data_dir=None, # replay buffer's dir offline_episode_len=None, # episode len of episodes stored in rb offline_ratio=0, # ratio of data that is from offline buffer ): g = tf.Graph() # register all gym envs max_steps_dict = { "HalfCheetahVel-v0": 50, "SawyerReach-v0": 40, "SawyerReachMT-v0": 40, "SawyerPeg-v0": 40, "SawyerPegMT-v0": 40, "SawyerPegMT4box-v0": 40, "SawyerShelfMT-v0": 40, "SawyerKitchenMT-v0": 40, "SawyerShelfMT-v2": 40, "SawyerButtons-v0": 40, } if max_episode_len_override: max_steps_dict[env_name] = max_episode_len_override register_all_gym_envs(max_steps_dict) # set max_episode_len based on our env max_episode_len = max_steps_dict[env_name] ###################################################### # Calculate additional params ###################################################### # convert to number of steps env_steps_per_trial = episodes_per_trial * max_episode_len real_env_steps_per_trial = episodes_per_trial * (max_episode_len + 1) env_steps_per_iter = num_tasks_to_collect_per_iter * collect_trials_per_task * env_steps_per_trial per_task_collect_steps = collect_trials_per_task * env_steps_per_trial # initial collect + train init_collect_env_steps = num_train_tasks * init_collect_trials_per_task * env_steps_per_trial init_model_train_steps = int(init_collect_env_steps * init_model_train_ratio) # collect + train collect_env_steps_per_iter = num_tasks_to_collect_per_iter * per_task_collect_steps model_train_steps_per_iter = int(env_steps_per_iter * model_train_ratio) ac_train_steps_per_iter = int(env_steps_per_iter * ac_train_ratio) # other global_steps_per_iter = collect_env_steps_per_iter + model_train_steps_per_iter + ac_train_steps_per_iter sample_episodes_per_task = train_trials_per_task * episodes_per_trial # number of episodes to sample from each replay model_bs_in_trials = model_bs_in_steps // real_env_steps_per_trial # assertions that make sure parameters make sense assert model_bs_in_trials > 0, "model batch size need to be at least as big as one full real trial" assert num_tasks_to_collect_per_iter <= num_train_tasks, "when sampling replace=False" assert num_tasks_per_train * train_trials_per_task >= model_bs_in_trials, "not enough data for one batch model train" assert num_tasks_per_train * train_trials_per_task * env_steps_per_trial >= ac_bs_in_steps, "not enough data for one batch ac train" ###################################################### # Print a summary of params ###################################################### MELD_summary_string = f"""\n\n\n ============================================================== ============================================================== \n MELD algorithm summary: * each trial consists of {episodes_per_trial} episodes * episode length: {max_episode_len}, trial length: {env_steps_per_trial} * {num_train_tasks} train tasks, {num_eval_tasks} eval tasks, hold-out: {eval_on_holdout_tasks} * environment: {env_name} For each of {num_train_tasks} tasks: Do {init_collect_trials_per_task} trials of initial collect (total {init_collect_env_steps} env steps) Do {init_model_train_steps} steps of initial model training For i in range(inf): For each of {num_tasks_to_collect_per_iter} randomly selected tasks: Do {collect_trials_per_task} trials of collect (which is {collect_trials_per_task*env_steps_per_trial} env steps per task) (for a total of {num_tasks_to_collect_per_iter*collect_trials_per_task*env_steps_per_trial} env steps in the iteration) if i % model_train_freq(={model_train_freq}): Do {model_train_steps_per_iter} steps of model training - select {sample_episodes_per_task} episodes from each of {num_tasks_per_train} random train_tasks, combine into {num_tasks_per_train*train_trials_per_task} total trials. - pick randomly {model_bs_in_trials} trials, train model on whole trials. if i % ac_train_freq(={ac_train_freq}): Do {ac_train_steps_per_iter} steps of ac training - select {sample_episodes_per_task} episodes from each of {num_tasks_per_train} random train_tasks, combine into {num_tasks_per_train*train_trials_per_task} total trials. - pick randomly {ac_bs_in_steps} transitions, not including between trial transitions, to train ac. * Other important params: Evaluate policy every {eval_interval} iters, equivalent to {global_steps_per_iter*eval_interval/1000:.1f}k global steps Average evaluation across {num_eval_trials} trials Save summary to tensorboard every {summary_freq_in_iter} iters, equivalent to {global_steps_per_iter*summary_freq_in_iter/1000:.1f}k global steps Checkpoint: - training checkpoint every {train_checkpoint_freq_in_iter} iters, equivalent to {global_steps_per_iter*train_checkpoint_freq_in_iter//1000}k global steps, keep 1 checkpoint - policy checkpoint every {policy_checkpoint_freq_in_iter} iters, equivalent to {global_steps_per_iter*policy_checkpoint_freq_in_iter//1000}k global steps, keep all checkpoints - replay buffer checkpoint every {rb_checkpoint_freq_in_iter} iters, equivalent to {global_steps_per_iter*rb_checkpoint_freq_in_iter//1000}k global steps, keep 1 checkpoint \n ============================================================= ============================================================= """ print(MELD_summary_string) time.sleep(1) ###################################################### # Seed + name + GPU configs + directories for saving ###################################################### np.random.seed(int(seed)) experiment_name += "_seed" + str(seed) gpus = tf.config.experimental.list_physical_devices('GPU') if gpu_allow_growth: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) if gpu_memory_limit: for gpu in gpus: tf.config.experimental.set_virtual_device_configuration( gpu, [ tf.config.experimental.VirtualDeviceConfiguration( memory_limit=gpu_memory_limit) ]) train_eval_dir = get_train_eval_dir(root_dir, universe, env_name, experiment_name) train_dir = os.path.join(train_eval_dir, 'train') eval_dir = os.path.join(train_eval_dir, 'eval') eval_dir_2 = os.path.join(train_eval_dir, 'eval2') ###################################################### # Train and Eval Summary Writers ###################################################### 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_summary_flush_op = eval_summary_writer.flush() eval_logger = Logger(eval_dir_2) ###################################################### # Train and Eval metrics ###################################################### eval_buffer_size = num_eval_trials * episodes_per_trial * max_episode_len # across all eval trials in each evaluation eval_metrics = [] for position in range( episodes_per_trial ): # have metrics for each episode position, to track whether it is learning eval_metrics_pos = [ py_metrics.AverageReturnMetric(name='c_AverageReturnEval_' + str(position), buffer_size=eval_buffer_size), py_metrics.AverageEpisodeLengthMetric( name='f_AverageEpisodeLengthEval_' + str(position), buffer_size=eval_buffer_size), custom_metrics.AverageScoreMetric( name="d_AverageScoreMetricEval_" + str(position), buffer_size=eval_buffer_size), ] eval_metrics.extend(eval_metrics_pos) train_buffer_size = num_train_tasks * episodes_per_trial train_metrics = [ tf_metrics.NumberOfEpisodes(name='NumberOfEpisodes'), tf_metrics.EnvironmentSteps(name='EnvironmentSteps'), tf_py_metric.TFPyMetric( py_metrics.AverageReturnMetric(name="a_AverageReturnTrain", buffer_size=train_buffer_size)), tf_py_metric.TFPyMetric( py_metrics.AverageEpisodeLengthMetric( name="e_AverageEpisodeLengthTrain", buffer_size=train_buffer_size)), tf_py_metric.TFPyMetric( custom_metrics.AverageScoreMetric(name="b_AverageScoreTrain", buffer_size=train_buffer_size)), ] global_step = tf.compat.v1.train.get_or_create_global_step( ) # will be use to record number of model grad steps + ac grad steps + env_step log_cond = get_log_condition_tensor( global_step, init_collect_trials_per_task, env_steps_per_trial, num_train_tasks, init_model_train_steps, collect_trials_per_task, num_tasks_to_collect_per_iter, model_train_steps_per_iter, ac_train_steps_per_iter, summary_freq_in_iter, eval_interval) with tf.compat.v2.summary.record_if(log_cond): ###################################################### # Create env ###################################################### py_env, eval_py_env, train_tasks, eval_tasks = load_environments( universe, action_mode, env_name=env_name, observations_whitelist=['state', 'pixels', "env_info"], action_repeat=action_repeat, num_train_tasks=num_train_tasks, num_eval_tasks=num_eval_tasks, eval_on_holdout_tasks=eval_on_holdout_tasks, return_multiple_tasks=True, ) override_reward_func = None if load_offline_data: py_env.set_task_dict(train_tasks) override_reward_func = py_env.override_reward_func tf_env = tf_py_environment.TFPyEnvironment(py_env, isolation=True) # Get data specs from env time_step_spec = tf_env.time_step_spec() observation_spec = time_step_spec.observation action_spec = tf_env.action_spec() original_control_timestep = get_control_timestep(eval_py_env) # fps control_timestep = original_control_timestep * float(action_repeat) render_fps = int(np.round(1.0 / original_control_timestep)) ###################################################### # Latent variable model ###################################################### if verbose: print("-- start constructing model networks --") model_net = ModelDistributionNetwork( double_camera=double_camera, observation_spec=observation_spec, num_repeat_when_concatenate=num_repeat_when_concatenate, task_reward_dim=task_reward_dim, episodes_per_trial=episodes_per_trial, max_episode_len=max_episode_len ) # rest of arguments provided via gin if verbose: print("-- finish constructing AC networks --") ###################################################### # Compressor Network for Actor/Critic # The model's compressor is also used by the AC # compressor function: images --> features ###################################################### compressor_net = model_net.compressor ###################################################### # Specs for Actor and Critic ###################################################### if actor_input == 'state': actor_state_size = observation_spec['state'].shape[0] elif actor_input == 'latentSample': actor_state_size = model_net.state_size elif actor_input == "latentDistribution": actor_state_size = 2 * model_net.state_size # mean and (diagonal) variance of gaussian, of two latents else: raise NotImplementedError actor_input_spec = tensor_spec.TensorSpec((actor_state_size, ), dtype=tf.float32) if critic_input == 'state': critic_state_size = observation_spec['state'].shape[0] elif critic_input == 'latentSample': critic_state_size = model_net.state_size elif critic_input == "latentDistribution": critic_state_size = 2 * model_net.state_size # mean and (diagonal) variance of gaussian, of two latents else: raise NotImplementedError critic_input_spec = tensor_spec.TensorSpec((critic_state_size, ), dtype=tf.float32) ###################################################### # Actor and Critic Networks ###################################################### if verbose: print("-- start constructing Actor and Critic networks --") actor_net = actor_distribution_network.ActorDistributionNetwork( actor_input_spec, action_spec, fc_layer_params=actor_fc_layers, ) critic_net = critic_network.CriticNetwork( (critic_input_spec, 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) if verbose: print("-- finish constructing AC networks --") print("-- start constructing agent --") ###################################################### # Create the agent ###################################################### which_posterior_overwrite = None which_reward_overwrite = None meld_agent = MeldAgent( # specs time_step_spec=time_step_spec, action_spec=action_spec, # step counter train_step_counter= global_step, # will count number of model training steps # networks actor_network=actor_net, critic_network=critic_net, model_network=model_net, compressor_network=compressor_net, # optimizers actor_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=actor_learning_rate), critic_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=critic_learning_rate), alpha_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=alpha_learning_rate), model_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=model_learning_rate), # target update target_update_tau=target_update_tau, target_update_period=target_update_period, # inputs critic_input=critic_input, actor_input=actor_input, # bs stuff model_batch_size=model_bs_in_steps, ac_batch_size=ac_bs_in_steps, # other num_tasks_per_train=num_tasks_per_train, td_errors_loss_fn=td_errors_loss_fn, gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, control_timestep=control_timestep, num_images_per_summary=num_images_per_summary, task_reward_dim=task_reward_dim, episodes_per_trial=episodes_per_trial, # offline data override_reward_func=override_reward_func, offline_ratio=offline_ratio, ) if verbose: print("-- finish constructing agent --") ###################################################### # Replay buffers + observers to add data to them ###################################################### replay_buffers = [] replay_observers = [] for _ in range(num_train_tasks): replay_buffer_episodic = episodic_replay_buffer.EpisodicReplayBuffer( meld_agent.collect_policy. trajectory_spec, # spec of each point stored in here (i.e. Trajectory) capacity=replay_buffer_capacity, completed_only= True, # in as_dataset, if num_steps is None, this means return full episodes # device='GPU:0', # gpu not supported for some reason begin_episode_fn=lambda traj: traj.is_first()[ 0], # first step of seq we add should be is_first end_episode_fn=lambda traj: traj.is_last()[ 0], # last step of seq we add should be is_last dataset_drop_remainder= True, #`as_dataset` makes the final batch be dropped if it does not contain exactly `sample_batch_size` items ) replay_buffer = StatefulEpisodicReplayBuffer( replay_buffer_episodic) # adding num_episodes here is bad replay_buffers.append(replay_buffer) replay_observers.append([replay_buffer.add_sequence]) if load_offline_data: # for each task, has a separate replay buffer for relabeled data replay_buffers_withRelabel = [] replay_observers_withRelabel = [] for _ in range(num_train_tasks): replay_buffer_episodic_withRelabel = episodic_replay_buffer.EpisodicReplayBuffer( meld_agent.collect_policy. trajectory_spec, # spec of each point stored in here (i.e. Trajectory) capacity=replay_buffer_capacity, completed_only= True, # in as_dataset, if num_steps is None, this means return full episodes # device='GPU:0', # gpu not supported for some reason begin_episode_fn=lambda traj: traj.is_first()[ 0], # first step of seq we add should be is_first end_episode_fn=lambda traj: traj.is_last()[ 0], # last step of seq we add should be is_last dataset_drop_remainder=True, # `as_dataset` makes the final batch be dropped if it does not contain exactly `sample_batch_size` items ) replay_buffer_withRelabel = StatefulEpisodicReplayBuffer( replay_buffer_episodic_withRelabel ) # adding num_episodes here is bad replay_buffers_withRelabel.append(replay_buffer_withRelabel) replay_observers_withRelabel.append( [replay_buffer_withRelabel.add_sequence]) if verbose: print("-- finish constructing replay buffers --") print("-- start constructing policies and collect ops --") ###################################################### # Policies ##################################################### # init collect policy (random) init_collect_policy = random_tf_policy.RandomTFPolicy( time_step_spec, action_spec) # eval eval_py_policy = py_tf_policy.PyTFPolicy(meld_agent.policy) ################################################################################ # Collect ops : use policies to get data + have the observer put data into corresponding RB ################################################################################ #init collection (with random policy) init_collect_ops = [] for task_idx in range(num_train_tasks): # put init data into the rb + track with the train metric observers = replay_observers[task_idx] + train_metrics # initial collect op init_collect_op = DynamicTrialDriver( tf_env, init_collect_policy, num_trials_to_collect=init_collect_trials_per_task, observers=observers, episodes_per_trial= episodes_per_trial, # policy state will not be reset within these episodes max_episode_len=max_episode_len, ).run() # collect one trial init_collect_ops.append(init_collect_op) # data collection for training (with collect policy) collect_ops = [] for task_idx in range(num_train_tasks): collect_op = DynamicTrialDriver( tf_env, meld_agent.collect_policy, num_trials_to_collect=collect_trials_per_task, observers=replay_observers[task_idx] + train_metrics, # put data into 1st RB + track with 1st pol metrics episodes_per_trial= episodes_per_trial, # policy state will not be reset within these episodes max_episode_len=max_episode_len, ).run() # collect one trial collect_ops.append(collect_op) if verbose: print("-- finish constructing policies and collect ops --") print("-- start constructing replay buffer->training pipeline --") ###################################################### # replay buffer --> dataset --> iterate to get trajecs for training ###################################################### # get some data from all task replay buffers (even though won't actually train on all of them) dataset_iterators = [] all_tasks_trajectories_fromdense = [] for task_idx in range(num_train_tasks): dataset = replay_buffers[task_idx].as_dataset( sample_batch_size= sample_episodes_per_task, # number of episodes to sample num_steps=max_episode_len + 1 ).prefetch( 3 ) # +1 to include the last state: a trajectory with n transition has n+1 states # iterator to go through the data dataset_iterator = tf.compat.v1.data.make_initializable_iterator( dataset) dataset_iterators.append(dataset_iterator) # get sample_episodes_per_task sequences, each of length num_steps trajectories_task_i, _ = dataset_iterator.get_next() all_tasks_trajectories_fromdense.append(trajectories_task_i) if load_offline_data: # have separate dataset for relabel data dataset_iterators_withRelabel = [] all_tasks_trajectories_fromdense_withRelabel = [] for task_idx in range(num_train_tasks): dataset = replay_buffers_withRelabel[task_idx].as_dataset( sample_batch_size= sample_episodes_per_task, # number of episodes to sample num_steps=offline_episode_len + 1 ).prefetch( 3 ) # +1 to include the last state: a trajectory with n transition has n+1 states # iterator to go through the data dataset_iterator = tf.compat.v1.data.make_initializable_iterator( dataset) dataset_iterators_withRelabel.append(dataset_iterator) # get sample_episodes_per_task sequences, each of length num_steps trajectories_task_i, _ = dataset_iterator.get_next() all_tasks_trajectories_fromdense_withRelabel.append( trajectories_task_i) if verbose: print("-- finish constructing replay buffer->training pipeline --") print("-- start constructing model and AC training ops --") ###################################### # Decoding latent samples into rewards ###################################### latent_samples_1_ph = tf.compat.v1.placeholder( dtype=tf.float32, shape=(None, None, meld_agent._model_network.latent1_size)) latent_samples_2_ph = tf.compat.v1.placeholder( dtype=tf.float32, shape=(None, None, meld_agent._model_network.latent2_size)) decode_rews_op = meld_agent._model_network.decode_latents_into_reward( latent_samples_1_ph, latent_samples_2_ph) ###################################### # Model/Actor/Critic train + summary ops ###################################### # train AC on data from replay buffer if load_offline_data: ac_train_op = meld_agent.train_ac_meld( all_tasks_trajectories_fromdense, all_tasks_trajectories_fromdense_withRelabel) else: ac_train_op = meld_agent.train_ac_meld( all_tasks_trajectories_fromdense) summary_ops = [] for train_metric in train_metrics: summary_ops.append( train_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2])) if verbose: print("-- finish constructing AC training ops --") ############################ # Model train + summary ops ############################ # train model on data from replay buffer if load_offline_data: model_train_op, check_step_types = meld_agent.train_model_meld( all_tasks_trajectories_fromdense, all_tasks_trajectories_fromdense_withRelabel) else: model_train_op, check_step_types = meld_agent.train_model_meld( all_tasks_trajectories_fromdense) model_summary_ops, model_summary_ops_2 = [], [] for summary_op in tf.compat.v1.summary.all_v2_summary_ops(): if summary_op not in summary_ops: model_summary_ops.append(summary_op) if verbose: print("-- finish constructing model training ops --") print("-- start constructing checkpointers --") ######################## # Eval metrics ######################## 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, step_metrics=train_metrics[:2]) ######################## # Create savers ######################## train_config_saver = gin.tf.GinConfigSaverHook(train_dir, summarize_config=False) eval_config_saver = gin.tf.GinConfigSaverHook(eval_dir, summarize_config=False) ######################## # Create checkpointers ######################## train_checkpointer = common.Checkpointer( ckpt_dir=train_dir, agent=meld_agent, global_step=global_step, metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'), max_to_keep=1) policy_checkpointer = common.Checkpointer( ckpt_dir=os.path.join(train_dir, 'policy'), policy=meld_agent.policy, global_step=global_step, max_to_keep=99999999999 ) # keep many policy checkpoints, in case of future eval rb_checkpointers = [] for buffer_idx in range(len(replay_buffers)): rb_checkpointer = common.Checkpointer( ckpt_dir=os.path.join(train_dir, 'replay_buffers/', "task" + str(buffer_idx)), max_to_keep=1, replay_buffer=replay_buffers[buffer_idx]) rb_checkpointers.append(rb_checkpointer) if load_offline_data: # for LOADING data not for checkpointing. No new data going in anyways rb_checkpointers_withRelabel = [] for buffer_idx in range(len(replay_buffers_withRelabel)): ckpt_dir = os.path.join(offline_data_dir, "task" + str(buffer_idx)) rb_checkpointer = common.Checkpointer( ckpt_dir=ckpt_dir, max_to_keep=99999999999, replay_buffer=replay_buffers_withRelabel[buffer_idx]) rb_checkpointers_withRelabel.append(rb_checkpointer) # Notice: these replay buffers need to follow the same sequence of tasks as the current one if verbose: print("-- finish constructing checkpointers --") print("-- start main training loop --") with tf.compat.v1.Session() as sess: ######################## # Initialize ######################## if eval_only: sess.run(eval_summary_writer.init()) load_eval_log( train_eval_dir=train_eval_dir, meld_agent=meld_agent, global_step=global_step, sess=sess, eval_metrics=eval_metrics, eval_py_env=eval_py_env, eval_py_policy=eval_py_policy, num_eval_trials=num_eval_trials, max_episode_len=max_episode_len, episodes_per_trial=episodes_per_trial, log_image_strips=log_image_strips, num_trials_to_render=num_trials_to_render, train_tasks= train_tasks, # in case want to eval on a train task eval_tasks=eval_tasks, model_net=model_net, render_fps=render_fps, decode_rews_op=decode_rews_op, latent_samples_1_ph=latent_samples_1_ph, latent_samples_2_ph=latent_samples_2_ph, ) return # Initialize checkpointing train_checkpointer.initialize_or_restore(sess) for rb_checkpointer in rb_checkpointers: rb_checkpointer.initialize_or_restore(sess) if load_offline_data: for rb_checkpointer in rb_checkpointers_withRelabel: rb_checkpointer.initialize_or_restore(sess) # Initialize dataset iterators for dataset_iterator in dataset_iterators: sess.run(dataset_iterator.initializer) if load_offline_data: for dataset_iterator in dataset_iterators_withRelabel: sess.run(dataset_iterator.initializer) # Initialize variables common.initialize_uninitialized_variables(sess) # Initialize summary writers sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) # Initialize savers train_config_saver.after_create_session(sess) eval_config_saver.after_create_session(sess) # Get value of step counter global_step_val = sess.run(global_step) if verbose: print("====== finished initialization ======") ################################################################ # If this is start of new exp (i.e., 1st step) and not continuing old exp # eval rand policy + do initial data collection ################################################################ fresh_start = (global_step_val == 0) if fresh_start: ######################## # Evaluate initial policy ######################## if eval_interval: logging.info( '\n\nDoing evaluation of initial policy on %d trials with randomly sampled tasks', num_eval_trials) perform_eval_and_summaries_meld( eval_metrics, eval_py_env, eval_py_policy, num_eval_trials, max_episode_len, episodes_per_trial, log_image_strips=log_image_strips, num_trials_to_render=num_eval_tasks, eval_tasks=eval_tasks, latent1_size=model_net.latent1_size, latent2_size=model_net.latent2_size, logger=eval_logger, global_step_val=global_step_val, render_fps=render_fps, decode_rews_op=decode_rews_op, latent_samples_1_ph=latent_samples_1_ph, latent_samples_2_ph=latent_samples_2_ph, log_image_observations=log_image_observations, ) sess.run(eval_summary_flush_op) logging.info( 'Done with evaluation of initial (random) policy.\n\n') ######################## # Initial data collection ######################## logging.info( '\n\nGlobal step %d: Beginning init collect op with random policy. Collecting %dx {%d, %d} trials for each task', global_step_val, init_collect_trials_per_task, max_episode_len, episodes_per_trial) init_increment_global_step_op = global_step.assign_add( env_steps_per_trial * init_collect_trials_per_task) for task_idx in range(num_train_tasks): logging.info('on task %d / %d', task_idx + 1, num_train_tasks) py_env.set_task_for_env(train_tasks[task_idx]) sess.run([ init_collect_ops[task_idx], init_increment_global_step_op ]) # incremented gs in granularity of task rb_checkpointer.save(global_step=global_step_val) logging.info('Finished init collect.\n\n') else: logging.info( '\n\nGlobal step %d from loaded experiment: Skipping init collect op.\n\n', global_step_val) ######################### # Create calls ######################### # [1] calls for running the policies to collect training data collect_calls = [] increment_global_step_op = global_step.assign_add( env_steps_per_trial * collect_trials_per_task) for task_idx in range(num_train_tasks): collect_calls.append( sess.make_callable( [collect_ops[task_idx], increment_global_step_op])) # [2] call for doing a training step (A + C) ac_train_step_call = sess.make_callable([ac_train_op, summary_ops]) # [3] call for doing a training step (model) model_train_step_call = sess.make_callable( [model_train_op, check_step_types, model_summary_ops]) # [4] call for evaluating what global_step number we're on global_step_call = sess.make_callable(global_step) # reset keeping track of steps/time 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') with train_summary_writer.as_default( ), tf.compat.v2.summary.record_if(True): steps_per_second_summary = tf.compat.v2.summary.scalar( name='global_steps_per_sec', data=steps_per_second_ph, step=global_step) ################################# # init model training ################################# if fresh_start: logging.info( '\n\nPerforming %d steps of init model training, each step on %d random tasks', init_model_train_steps, num_tasks_per_train) for i in range(init_model_train_steps): temp_start = time.time() if i % 100 == 0: print(".... init model training ", i, "/", init_model_train_steps) # init model training total_loss_value_model, check_step_types, _ = model_train_step_call( ) if PRINT_TIMING: print("single model train step: ", time.time() - temp_start) if verbose: print("\n\n\n-- start training loop --\n") ################################# # Training Loop ################################# start_time = time.time() for iteration in range(num_iterations): if iteration > 0: g.finalize() # print("\n\n\niter", iteration, sess.run(curr_iter)) print("global step", global_step_call()) logging.info("Iteration: %d, Global step: %d\n", iteration, global_step_val) #################### # collect data #################### logging.info( '\nStarting batch data collection. Collecting %d {%d, %d} trials for each of %d tasks', collect_trials_per_task, max_episode_len, episodes_per_trial, num_tasks_to_collect_per_iter) # randomly select tasks to collect this iteration list_of_collect_task_idxs = np.random.choice( len(train_tasks), num_tasks_to_collect_per_iter, replace=False) for count, task_idx in enumerate(list_of_collect_task_idxs): logging.info('on randomly selected task %d / %d', count + 1, num_tasks_to_collect_per_iter) # set task for the env py_env.set_task_for_env(train_tasks[task_idx]) # collect data with collect policy _, policy_state_val = collect_calls[task_idx]() logging.info('Finish data collection. Global step: %d\n', global_step_call()) #################### # train model #################### if (iteration == 0) or ((iteration % model_train_freq == 0) and (global_step_val < stop_model_training)): logging.info( '\n\nPerforming %d steps of model training, each on %d random tasks', model_train_steps_per_iter, num_tasks_per_train) for model_iter in range(model_train_steps_per_iter): temp_start_2 = time.time() # train model total_loss_value_model, _, _ = model_train_step_call() # print("is logging step", model_iter, sess.run(is_logging_step)) if PRINT_TIMING: print("2: single model train step: ", time.time() - temp_start_2) logging.info('Finish model training. Global step: %d\n', global_step_call()) else: print("SKIPPING MODEL TRAINING") #################### # train actor critic #################### if iteration % ac_train_freq == 0: logging.info( '\n\nPerforming %d steps of AC training, each on %d random tasks \n\n', ac_train_steps_per_iter, num_tasks_per_train) for ac_iter in range(ac_train_steps_per_iter): temp_start_2_ac = time.time() # train ac total_loss_value_ac, _ = ac_train_step_call() if PRINT_TIMING: print("2: single AC train step: ", time.time() - temp_start_2_ac) logging.info('Finish AC training. Global step: %d\n', global_step_call()) # add up time time_acc += time.time() - start_time #################### # logging/summaries #################### ### Eval if eval_interval and (iteration % eval_interval == 0): logging.info( '\n\nDoing evaluation of trained policy on %d trials with randomly sampled tasks', num_eval_trials) perform_eval_and_summaries_meld( eval_metrics, eval_py_env, eval_py_policy, num_eval_trials, max_episode_len, episodes_per_trial, log_image_strips=log_image_strips, num_trials_to_render= num_trials_to_render, # hardcoded: or gif will get too long eval_tasks=eval_tasks, latent1_size=model_net.latent1_size, latent2_size=model_net.latent2_size, logger=eval_logger, global_step_val=global_step_call(), render_fps=render_fps, decode_rews_op=decode_rews_op, latent_samples_1_ph=latent_samples_1_ph, latent_samples_2_ph=latent_samples_2_ph, log_image_observations=log_image_observations, ) ### steps_per_second_summary global_step_val = global_step_call() if logging_freq_in_iter and (iteration % logging_freq_in_iter == 0): # log step number + speed (steps/sec) logging.info( 'step = %d, loss = %f', global_step_val, total_loss_value_ac.loss + total_loss_value_model.loss) steps_per_sec = (global_step_val - timed_at_step) / time_acc logging.info('%.3f env_steps/sec', steps_per_sec) sess.run(steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) # reset keeping track of steps/time timed_at_step = global_step_val time_acc = 0 ### train_checkpoint if train_checkpoint_freq_in_iter and ( iteration % train_checkpoint_freq_in_iter == 0): train_checkpointer.save(global_step=global_step_val) ### policy_checkpointer if policy_checkpoint_freq_in_iter and ( iteration % policy_checkpoint_freq_in_iter == 0): policy_checkpointer.save(global_step=global_step_val) ### rb_checkpointer if rb_checkpoint_freq_in_iter and ( iteration % rb_checkpoint_freq_in_iter == 0): for rb_checkpointer in rb_checkpointers: rb_checkpointer.save(global_step=global_step_val)
def train_eval( root_dir, env_name='CartPole-v0', num_iterations=1000, # TODO(kbanoop): rename to policy_fc_layers. actor_fc_layers=(100, ), # Params for collect collect_episodes_per_iteration=2, replay_buffer_capacity=2000, # Params for train learning_rate=1e-3, gradient_clipping=None, normalize_returns=True, # Params for eval num_eval_episodes=10, eval_interval=100, # Params for checkpoints, summaries, and logging train_checkpoint_interval=100, policy_checkpoint_interval=100, rb_checkpoint_interval=200, log_interval=100, summary_interval=100, summaries_flush_secs=1, debug_summaries=True, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for Reinforce.""" 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), ] 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)): eval_py_env = suite_gym.load(env_name) tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env_name)) # TODO(kbanoop): Handle distributions without gin. actor_net = actor_distribution_network.ActorDistributionNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), fc_layer_params=actor_fc_layers) tf_agent = reinforce_agent.ReinforceAgent( tf_env.time_step_spec(), tf_env.action_spec(), actor_network=actor_net, optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=learning_rate), normalize_returns=normalize_returns, gradient_clipping=gradient_clipping, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, 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 collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_episodes=collect_episodes_per_iteration).run() experience = replay_buffer.gather_all() train_op = tf_agent.train(experience) clear_rb_op = replay_buffer.clear() 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) for train_metric in train_metrics: 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() 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) # TODO(sguada) Remove once Periodically can be saved. common.initialize_uninitialized_variables(sess) sess.run(init_agent_op) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) # Compute evaluation metrics. global_step_call = sess.make_callable(global_step) global_step_val = global_step_call() 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) clear_rb_call = sess.make_callable(clear_rb_op) 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.contrib.summary.scalar( name='global_steps/sec', tensor=steps_per_second_ph) for _ in range(num_iterations): start_time = time.time() collect_call() total_loss = train_step_call() clear_rb_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, total_loss.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, )
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, tf_master='', env_name='HalfCheetah-v2', env_load_fn=suite_mujoco.load, random_seed=0, # TODO(b/127576522): rename to policy_fc_layers. actor_fc_layers=(200, 100), value_fc_layers=(200, 100), use_rnns=False, # Params for collect num_environment_steps=10000000, collect_episodes_per_iteration=30, num_parallel_environments=30, replay_buffer_capacity=1001, # Per-environment # Params for train num_epochs=25, learning_rate=1e-4, # Params for eval num_eval_episodes=30, eval_interval=500, # Params for summaries and logging train_checkpoint_interval=100, policy_checkpoint_interval=50, rb_checkpoint_interval=200, log_interval=50, summary_interval=50, summaries_flush_secs=1, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None): """A simple train and eval for PPO.""" if root_dir is None: raise AttributeError('train_eval requires a root_dir.') 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 = [ batched_py_metric.BatchedPyMetric( AverageReturnMetric, metric_args={'buffer_size': num_eval_episodes}, batch_size=num_parallel_environments), batched_py_metric.BatchedPyMetric( AverageEpisodeLengthMetric, metric_args={'buffer_size': num_eval_episodes}, batch_size=num_parallel_environments), ] eval_summary_writer_flush_op = eval_summary_writer.flush() 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)): tf.compat.v1.set_random_seed(random_seed) eval_py_env = parallel_py_environment.ParallelPyEnvironment( [lambda: env_load_fn(env_name)] * num_parallel_environments) tf_env = tf_py_environment.TFPyEnvironment( parallel_py_environment.ParallelPyEnvironment( [lambda: env_load_fn(env_name)] * num_parallel_environments)) optimizer = tf.compat.v1.train.AdamOptimizer( learning_rate=learning_rate) if use_rnns: actor_net = actor_distribution_rnn_network.ActorDistributionRnnNetwork( tf_env.observation_spec(), tf_env.action_spec(), input_fc_layer_params=actor_fc_layers, output_fc_layer_params=None) value_net = value_rnn_network.ValueRnnNetwork( tf_env.observation_spec(), input_fc_layer_params=value_fc_layers, output_fc_layer_params=None) else: actor_net = actor_distribution_network.ActorDistributionNetwork( tf_env.observation_spec(), tf_env.action_spec(), fc_layer_params=actor_fc_layers) value_net = value_network.ValueNetwork( tf_env.observation_spec(), fc_layer_params=value_fc_layers) tf_agent = ppo_agent.PPOAgent( tf_env.time_step_spec(), tf_env.action_spec(), optimizer, actor_net=actor_net, value_net=value_net, num_epochs=num_epochs, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, train_step_counter=global_step) replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec, batch_size=num_parallel_environments, max_length=replay_buffer_capacity) eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy) environment_steps_metric = tf_metrics.EnvironmentSteps() environment_steps_count = environment_steps_metric.result() step_metrics = [ tf_metrics.NumberOfEpisodes(), environment_steps_metric, ] train_metrics = step_metrics + [ tf_metrics.AverageReturnMetric(), tf_metrics.AverageEpisodeLengthMetric(), ] # Add to replay buffer and other agent specific observers. replay_buffer_observer = [replay_buffer.add_batch] collect_policy = tf_agent.collect_policy collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=replay_buffer_observer + train_metrics, num_episodes=collect_episodes_per_iteration).run() trajectories = replay_buffer.gather_all() train_op, _ = tf_agent.train(experience=trajectories) with tf.control_dependencies([train_op]): clear_replay_op = replay_buffer.clear() with tf.control_dependencies([clear_replay_op]): train_op = tf.identity(train_op) 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) for train_metric in train_metrics: train_metric.tf_summaries(train_step=global_step, step_metrics=step_metrics) with eval_summary_writer.as_default(), \ tf.compat.v2.summary.record_if(True): for eval_metric in eval_metrics: eval_metric.tf_summaries(step_metrics=step_metrics) init_agent_op = tf_agent.initialize() with tf.compat.v1.Session(tf_master) as sess: # Initialize graph. train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) common.initialize_uninitialized_variables(sess) sess.run(init_agent_op) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) collect_time = 0 train_time = 0 timed_at_step = sess.run(global_step) steps_per_second_ph = tf.compat.v1.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) while sess.run(environment_steps_count) < num_environment_steps: global_step_val = sess.run(global_step) 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, ) sess.run(eval_summary_writer_flush_op) start_time = time.time() sess.run(collect_op) collect_time += time.time() - start_time start_time = time.time() total_loss = sess.run(train_op) train_time += time.time() - start_time global_step_val = sess.run(global_step) if global_step_val % log_interval == 0: logging.info('step = %d, loss = %f', global_step_val, total_loss) steps_per_sec = ((global_step_val - timed_at_step) / (collect_time + train_time)) logging.info('%.3f steps/sec', steps_per_sec) sess.run(steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) logging.info( '%s', 'collect_time = {}, train_time = {}'.format( collect_time, train_time)) timed_at_step = global_step_val collect_time = 0 train_time = 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) # One final eval before exiting. 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, ) sess.run(eval_summary_writer_flush_op)
def train_eval( root_dir, experiment_name, train_eval_dir=None, universe='gym', env_name='HalfCheetah-v2', domain_name='cheetah', task_name='run', action_repeat=1, num_iterations=int(1e7), actor_fc_layers=(256, 256), critic_obs_fc_layers=None, critic_action_fc_layers=None, critic_joint_fc_layers=(256, 256), model_network_ctor=model_distribution_network.ModelDistributionNetwork, critic_input='state', actor_input='state', compressor_descriptor='preprocessor_32_3', # Params for collect initial_collect_steps=10000, collect_steps_per_iteration=1, replay_buffer_capacity=int(1e5), # increase if necessary since buffers with images are huge # Params for target update target_update_tau=0.005, target_update_period=1, # Params for train train_steps_per_iteration=1, model_train_steps_per_iteration=1, initial_model_train_steps=100000, batch_size=256, model_batch_size=32, sequence_length=4, actor_learning_rate=3e-4, critic_learning_rate=3e-4, alpha_learning_rate=3e-4, model_learning_rate=1e-4, td_errors_loss_fn=functools.partial( tf.compat.v1.losses.mean_squared_error, weights=0.5), gamma=0.99, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval num_eval_episodes=10, eval_interval=10000, # Params for summaries and logging num_images_per_summary=1, train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=0, # enable if necessary since buffers with images are huge log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, summarize_grads_and_vars=False, gpu_allow_growth=False, gpu_memory_limit=None): """A simple train and eval for SLAC.""" gpus = tf.config.experimental.list_physical_devices('GPU') if gpu_allow_growth: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) if gpu_memory_limit: for gpu in gpus: tf.config.experimental.set_virtual_device_configuration( gpu, [ tf.config.experimental.VirtualDeviceConfiguration( memory_limit=gpu_memory_limit) ]) if train_eval_dir is None: train_eval_dir = get_train_eval_dir(root_dir, universe, env_name, domain_name, task_name, experiment_name) train_dir = os.path.join(train_eval_dir, 'train') eval_dir = os.path.join(train_eval_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(name='AverageReturnEvalPolicy', buffer_size=num_eval_episodes), py_metrics.AverageEpisodeLengthMetric( name='AverageEpisodeLengthEvalPolicy', buffer_size=num_eval_episodes), ] eval_greedy_metrics = [ py_metrics.AverageReturnMetric(name='AverageReturnEvalGreedyPolicy', buffer_size=num_eval_episodes), py_metrics.AverageEpisodeLengthMetric( name='AverageEpisodeLengthEvalGreedyPolicy', buffer_size=num_eval_episodes), ] eval_summary_flush_op = eval_summary_writer.flush() 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)): # Create the environment. trainable_model = model_train_steps_per_iteration != 0 state_only = (actor_input == 'state' and critic_input == 'state' and not trainable_model and initial_model_train_steps == 0) # Save time from unnecessarily rendering observations. observations_whitelist = ['state'] if state_only else None py_env, eval_py_env = load_environments( universe, env_name=env_name, domain_name=domain_name, task_name=task_name, observations_whitelist=observations_whitelist, action_repeat=action_repeat) tf_env = tf_py_environment.TFPyEnvironment(py_env, isolation=True) original_control_timestep = get_control_timestep(eval_py_env) control_timestep = original_control_timestep * float(action_repeat) fps = int(np.round(1.0 / control_timestep)) render_fps = int(np.round(1.0 / original_control_timestep)) # Get the data specs from the environment time_step_spec = tf_env.time_step_spec() observation_spec = time_step_spec.observation action_spec = tf_env.action_spec() if model_train_steps_per_iteration not in (0, train_steps_per_iteration): raise NotImplementedError model_net = model_network_ctor(observation_spec, action_spec) if compressor_descriptor == 'model': compressor_net = model_net.compressor elif re.match('preprocessor_(\d+)_(\d+)', compressor_descriptor): m = re.match('preprocessor_(\d+)_(\d+)', compressor_descriptor) filters, n_layers = m.groups() filters = int(filters) n_layers = int(n_layers) compressor_net = compressor_network.Preprocessor(filters, n_layers=n_layers) elif re.match('compressor_(\d+)', compressor_descriptor): m = re.match('compressor_(\d+)', compressor_descriptor) filters, = m.groups() filters = int(filters) compressor_net = compressor_network.Compressor(filters) elif re.match('softlearning_(\d+)_(\d+)', compressor_descriptor): m = re.match('softlearning_(\d+)_(\d+)', compressor_descriptor) filters, n_layers = m.groups() filters = int(filters) n_layers = int(n_layers) compressor_net = compressor_network.SoftlearningPreprocessor( filters, n_layers=n_layers) elif compressor_descriptor == 'd4pg': compressor_net = compressor_network.D4pgPreprocessor() else: raise NotImplementedError(compressor_descriptor) actor_state_size = 0 for _actor_input in actor_input.split('__'): if _actor_input == 'state': state_size, = observation_spec['state'].shape actor_state_size += state_size elif _actor_input == 'latent': actor_state_size += model_net.state_size elif _actor_input == 'feature': actor_state_size += compressor_net.feature_size elif _actor_input in ('sequence_feature', 'sequence_action_feature'): actor_state_size += compressor_net.feature_size * sequence_length if _actor_input == 'sequence_action_feature': actor_state_size += tf.compat.dimension_value( action_spec.shape[0]) * (sequence_length - 1) else: raise NotImplementedError actor_input_spec = tensor_spec.TensorSpec((actor_state_size, ), dtype=tf.float32) critic_state_size = 0 for _critic_input in critic_input.split('__'): if _critic_input == 'state': state_size, = observation_spec['state'].shape critic_state_size += state_size elif _critic_input == 'latent': critic_state_size += model_net.state_size elif _critic_input == 'feature': critic_state_size += compressor_net.feature_size elif _critic_input in ('sequence_feature', 'sequence_action_feature'): critic_state_size += compressor_net.feature_size * sequence_length if _critic_input == 'sequence_action_feature': critic_state_size += tf.compat.dimension_value( action_spec.shape[0]) * (sequence_length - 1) else: raise NotImplementedError critic_input_spec = tensor_spec.TensorSpec((critic_state_size, ), dtype=tf.float32) actor_net = actor_distribution_network.ActorDistributionNetwork( actor_input_spec, action_spec, fc_layer_params=actor_fc_layers) critic_net = critic_network.CriticNetwork( (critic_input_spec, 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) tf_agent = slac_agent.SlacAgent( time_step_spec, action_spec, actor_network=actor_net, critic_network=critic_net, model_network=model_net, compressor_network=compressor_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), alpha_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=alpha_learning_rate), model_optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=model_learning_rate), sequence_length=sequence_length, target_update_tau=target_update_tau, target_update_period=target_update_period, td_errors_loss_fn=td_errors_loss_fn, gamma=gamma, reward_scale_factor=reward_scale_factor, gradient_clipping=gradient_clipping, trainable_model=trainable_model, critic_input=critic_input, actor_input=actor_input, model_batch_size=model_batch_size, control_timestep=control_timestep, num_images_per_summary=num_images_per_summary, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, train_step_counter=global_step) # Make the replay buffer. replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( data_spec=tf_agent.collect_data_spec, batch_size=1, max_length=replay_buffer_capacity) replay_observer = [replay_buffer.add_batch] eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy) eval_greedy_py_policy = py_tf_policy.PyTFPolicy( greedy_policy.GreedyPolicy(tf_agent.policy)) train_metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.EnvironmentSteps(), tf_py_metric.TFPyMetric( py_metrics.AverageReturnMetric(buffer_size=1)), tf_py_metric.TFPyMetric( py_metrics.AverageEpisodeLengthMetric(buffer_size=1)), ] collect_policy = tf_agent.collect_policy initial_collect_policy = random_tf_policy.RandomTFPolicy( time_step_spec, action_spec) initial_policy_state = initial_collect_policy.get_initial_state(1) initial_collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, initial_collect_policy, observers=replay_observer + train_metrics, num_steps=initial_collect_steps).run( policy_state=initial_policy_state) policy_state = collect_policy.get_initial_state(1) collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=replay_observer + train_metrics, num_steps=collect_steps_per_iteration).run( policy_state=policy_state) # Prepare replay buffer as dataset with invalid transitions filtered. def _filter_invalid_transition(trajectories, unused_arg1): return ~trajectories.is_boundary()[-2] dataset = replay_buffer.as_dataset( num_parallel_calls=3, sample_batch_size=batch_size, num_steps=sequence_length + 1).unbatch().filter(_filter_invalid_transition).batch( batch_size, drop_remainder=True).prefetch(3) dataset_iterator = tf.compat.v1.data.make_initializable_iterator( dataset) trajectories, unused_info = dataset_iterator.get_next() train_op = tf_agent.train(trajectories) summary_ops = [] for train_metric in train_metrics: summary_ops.append( train_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2])) if initial_model_train_steps: with tf.name_scope('initial'): model_train_op = tf_agent.train_model(trajectories) model_summary_ops = [] for summary_op in tf.compat.v1.summary.all_v2_summary_ops(): if summary_op not in summary_ops: model_summary_ops.append(summary_op) with eval_summary_writer.as_default(), \ tf.compat.v2.summary.record_if(True): for eval_metric in eval_metrics + eval_greedy_metrics: eval_metric.tf_summaries(train_step=global_step, step_metrics=train_metrics[:2]) if eval_interval: eval_images_ph = tf.compat.v1.placeholder(dtype=tf.uint8, shape=[None] * 5) eval_images_summary = gif_utils.gif_summary_v2( 'ObservationVideoEvalPolicy', eval_images_ph, 1, fps) eval_render_images_summary = gif_utils.gif_summary_v2( 'VideoEvalPolicy', eval_images_ph, 1, render_fps) eval_greedy_images_summary = gif_utils.gif_summary_v2( 'ObservationVideoEvalGreedyPolicy', eval_images_ph, 1, fps) eval_greedy_render_images_summary = gif_utils.gif_summary_v2( 'VideoEvalGreedyPolicy', eval_images_ph, 1, render_fps) train_config_saver = gin.tf.GinConfigSaverHook(train_dir, summarize_config=False) eval_config_saver = gin.tf.GinConfigSaverHook(eval_dir, summarize_config=False) train_checkpointer = common.Checkpointer( ckpt_dir=train_dir, agent=tf_agent, global_step=global_step, metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'), max_to_keep=2) policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join( train_dir, 'policy'), policy=tf_agent.policy, global_step=global_step, max_to_keep=2) rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join( train_dir, 'replay_buffer'), max_to_keep=1, replay_buffer=replay_buffer) with tf.compat.v1.Session() as sess: # Initialize graph. train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) # Initialize training. sess.run(dataset_iterator.initializer) common.initialize_uninitialized_variables(sess) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) train_config_saver.after_create_session(sess) eval_config_saver.after_create_session(sess) global_step_val = sess.run(global_step) if global_step_val == 0: if eval_interval: # Initial eval of randomly initialized policy for _eval_metrics, _eval_py_policy, \ _eval_render_images_summary, _eval_images_summary in ( (eval_metrics, eval_py_policy, eval_render_images_summary, eval_images_summary), (eval_greedy_metrics, eval_greedy_py_policy, eval_greedy_render_images_summary, eval_greedy_images_summary)): compute_summaries( _eval_metrics, eval_py_env, _eval_py_policy, num_episodes=num_eval_episodes, num_episodes_to_render=num_images_per_summary, images_ph=eval_images_ph, render_images_summary=_eval_render_images_summary, images_summary=_eval_images_summary) sess.run(eval_summary_flush_op) # Run initial collect. logging.info('Global step %d: Running initial collect op.', global_step_val) sess.run(initial_collect_op) # Checkpoint the initial replay buffer contents. rb_checkpointer.save(global_step=global_step_val) logging.info('Finished initial collect.') else: logging.info('Global step %d: Skipping initial collect op.', global_step_val) policy_state_val = sess.run(policy_state) collect_call = sess.make_callable(collect_op, feed_list=[policy_state]) train_step_call = sess.make_callable([train_op, summary_ops]) if initial_model_train_steps: model_train_step_call = sess.make_callable( [model_train_op, model_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 should always be recorded since it's only called every log_interval steps with tf.compat.v2.summary.record_if(True): steps_per_second_summary = tf.compat.v2.summary.scalar( name='global_steps_per_sec', data=steps_per_second_ph, step=global_step) for iteration in range(global_step_val, initial_model_train_steps + num_iterations): start_time = time.time() if iteration < initial_model_train_steps: total_loss_val, _ = model_train_step_call() else: time_step_val, policy_state_val = collect_call( policy_state_val) for _ in range(train_steps_per_iteration): total_loss_val, _ = train_step_call() time_acc += time.time() - start_time global_step_val = global_step_call() if log_interval and global_step_val % log_interval == 0: logging.info('step = %d, loss = %f', global_step_val, total_loss_val.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 (train_checkpoint_interval and global_step_val % train_checkpoint_interval == 0): train_checkpointer.save(global_step=global_step_val) if iteration < initial_model_train_steps: continue if eval_interval and global_step_val % eval_interval == 0: for _eval_metrics, _eval_py_policy, \ _eval_render_images_summary, _eval_images_summary in ( (eval_metrics, eval_py_policy, eval_render_images_summary, eval_images_summary), (eval_greedy_metrics, eval_greedy_py_policy, eval_greedy_render_images_summary, eval_greedy_images_summary)): compute_summaries( _eval_metrics, eval_py_env, _eval_py_policy, num_episodes=num_eval_episodes, num_episodes_to_render=num_images_per_summary, images_ph=eval_images_ph, render_images_summary=_eval_render_images_summary, images_summary=_eval_images_summary) sess.run(eval_summary_flush_op) if (policy_checkpoint_interval and global_step_val % policy_checkpoint_interval == 0): policy_checkpointer.save(global_step=global_step_val) if (rb_checkpoint_interval and global_step_val % rb_checkpoint_interval == 0): rb_checkpointer.save(global_step=global_step_val)
def train_eval( root_dir, env_name='HalfCheetah-v1', env_load_fn=suite_mujoco.load, num_iterations=2000000, actor_fc_layers=(400, 300), critic_obs_fc_layers=(400,), critic_action_fc_layers=None, critic_joint_fc_layers=(300,), # Params for collect initial_collect_steps=1000, collect_steps_per_iteration=1, num_parallel_environments=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=1, batch_size=64, actor_learning_rate=1e-4, critic_learning_rate=1e-3, dqda_clipping=None, td_errors_loss_fn=tf.losses.huber_loss, gamma=0.995, reward_scale_factor=1.0, gradient_clipping=None, # Params for eval num_eval_episodes=10, eval_interval=10000, # Params for checkpoints, summaries, and logging train_checkpoint_interval=10000, policy_checkpoint_interval=5000, rb_checkpoint_interval=20000, log_interval=1000, summary_interval=1000, summaries_flush_secs=10, debug_summaries=False, summarize_grads_and_vars=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 num_parallel_environments > 1: tf_env = tf_py_environment.TFPyEnvironment( parallel_py_environment.ParallelPyEnvironment( [lambda: env_load_fn(env_name)] * num_parallel_environments)) else: tf_env = tf_py_environment.TFPyEnvironment(env_load_fn(env_name)) eval_py_env = env_load_fn(env_name) actor_net = actor_network.ActorNetwork( tf_env.time_step_spec().observation, tf_env.action_spec(), fc_layer_params=actor_fc_layers, ) critic_net_input_specs = (tf_env.time_step_spec().observation, tf_env.action_spec()) critic_net = critic_network.CriticNetwork( 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, ) 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.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, 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) 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() collect_policy = tf_agent.collect_policy() initial_collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch], num_steps=initial_collect_steps).run() collect_op = dynamic_step_driver.DynamicStepDriver( tf_env, collect_policy, observers=[replay_buffer.add_batch] + train_metrics, num_steps=collect_steps_per_iteration).run() # Dataset generates trajectories with shape [Bx2x...] dataset = replay_buffer.as_dataset( num_parallel_calls=3, sample_batch_size=batch_size, num_steps=2).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, summary_dir, game_config, tf_master='', env_load_fn=None, random_seed=0, # TODO(b/127576522): rename to policy_fc_layers. actor_fc_layers=(150, 75), value_fc_layers=(150, 75), actor_fc_layers_rnn=(150, ), value_fc_layers_rnn=(150, ), use_rnns=True, # Params for collect num_environment_steps=int(3e08), collect_episodes_per_iteration=90, num_parallel_environments=30, replay_buffer_capacity=1001, # Per-environment # Params for train num_epochs=25, learning_rate=1e-4, # Params for eval num_eval_episodes=30, eval_interval=5000, # Params for summaries and logging train_checkpoint_interval=2000, policy_checkpoint_interval=1000, rb_checkpoint_interval=4000, log_interval=500, summary_interval=500, summaries_flush_secs=1, debug_summaries=False, summarize_grads_and_vars=False, eval_metrics_callback=None, eval_py_env=None, tf_env=None): tf.reset_default_graph() """A simple train and eval for PPO.""" if root_dir is None: raise AttributeError('train_eval requires a root_dir.') # ################################################ # # ------------ Create summary-writers ------------ # # ################################################ # root_dir = os.path.expanduser(root_dir) summary_dir = os.path.join(summary_dir, FOLDERNAME) train_dir = os.path.join(os.path.join(root_dir, 'train'), FOLDERNAME) eval_dir = os.path.join(os.path.join(root_dir, 'eval'), FOLDERNAME) train_summary_writer, eval_summary_writer = get_writers_train_eval( summary_dir, eval_dir, filename_suffix=FILENAME_SUFFIX) eval_metrics = get_metrics_eval(num_parallel_environments, num_eval_episodes) eval_summary_writer_flush_op = eval_summary_writer.flush() 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)): tf.compat.v1.set_random_seed(random_seed) optimizer = tf.compat.v1.train.AdamOptimizer( learning_rate=learning_rate) # ################################################ # # ---------------- Create Networks --------------- # # ################################################ # if use_rnns: actor_net, value_net = get_networks( tf_env, { "actor_net": actor_fc_layers_rnn, "value_net": value_fc_layers_rnn }) else: actor_net, value_net = get_networks(tf_env, { "actor_net": actor_fc_layers, "value_net": value_fc_layers }) state_pred_net = custom_environment.predictive_models.StatePredictor( state_pred_l1, state_pred_l2, num_parallel_environments, curiosity_param) action_pred_net = custom_environment.predictive_models.ActionPredictor( action_pred_l1, action_pred_l2, action_pred_l3) #traj = trajectory.Trajectory( # step_type=[], # observation=[], # action=[], # policy_info=[], # next_step_type=[], # reward=[], # discount=[]) # ################################################ # # ---------------- Create PPO Agent -------------- # # ################################################ # tf_agent = ppo_agent.PPOAgent( tf_env.time_step_spec(), tf_env.action_spec(), optimizer, entropy_regularization=0, #0.1 up to 0.4 actor_net=actor_net, value_net=value_net, num_epochs=num_epochs, debug_summaries=debug_summaries, summarize_grads_and_vars=summarize_grads_and_vars, train_step_counter=global_step, normalize_observations=False ) # cause the observations also include the 0-1 mask replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer( tf_agent.collect_data_spec, batch_size=num_parallel_environments, max_length=replay_buffer_capacity) eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy) # ################################################ # # ---------------- Create Metrics ---------------- # # ################################################ # train_metrics, step_metrics, environment_steps_count = get_metrics_train_and_step( num_eval_episodes, num_parallel_environments) # Add to replay buffer and other agent specific observers. replay_buffer_observer = [replay_buffer.add_batch] # ################################################ # # ----------------- Trajectories ----------------- # # ################################################ # collect_policy = tf_agent.collect_policy collect_op = dynamic_episode_driver.DynamicEpisodeDriver( tf_env, collect_policy, observers=replay_buffer_observer + train_metrics, num_episodes=collect_episodes_per_iteration).run() trajectories = replay_buffer.gather_all() train_op, _ = tf_agent.train(trajectories) # Prediction Implementation OPs gather_op = replay_buffer.gather_all() clear_op = replay_buffer.clear() step_type = tf.placeholder("int32", None) state = tf.placeholder("uint8", [None, None]) info = tf.placeholder("int64", None) mask = tf.placeholder("float32", [None, None]) state2 = tf.placeholder("uint8", [None, None]) action = tf.placeholder("int64", None) logits = tf.placeholder("float32", [None, None]) next_step_type = tf.placeholder("int32", None) reward = tf.placeholder("float32", None) discount = tf.placeholder("float32", None) traj = trajectory.Trajectory(step_type=step_type, observation={ 'state': state, 'mask': mask, 'info': info, 'state2': state2 }, action=action, policy_info={'logits': logits}, next_step_type=next_step_type, reward=reward, discount=discount) add_batch_op = replay_buffer.add_batch(traj) # printing #print_op = tf.print(trajectories) #with tf.control_dependencies([print_op]): # train_op, _ = tf_agent.train(trajectories) with tf.control_dependencies([train_op]): clear_replay_op = replay_buffer.clear() with tf.control_dependencies([clear_replay_op]): train_op = tf.identity(train_op) # ################################################ # # ------------ Create Checkpointers -------------- # # ################################################ # 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) # ################################################ # # -------------- Create Summary Ops -------------- # # ################################################ # summary_ops = [] for train_metric in train_metrics: summary_ops.append( train_metric.tf_summaries(train_step=global_step, step_metrics=step_metrics)) 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, step_metrics=step_metrics) init_agent_op = tf_agent.initialize() # ################################################ # # --------------- Initialize Graph --------------- # # ################################################ # with tf.compat.v1.Session(tf_master) as sess: train_checkpointer.initialize_or_restore(sess) rb_checkpointer.initialize_or_restore(sess) common.initialize_uninitialized_variables(sess) sess.run(init_agent_op) sess.run(train_summary_writer.init()) sess.run(eval_summary_writer.init()) collect_time = 0 train_time = 0 timed_at_step = sess.run(global_step) 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) # ################################################ # # -------------------- Loop ------ --------------- # # ------------ Collect/Train/Write --------------- # # ################################################ # while sess.run(environment_steps_count) < num_environment_steps: global_step_val = sess.run(global_step) 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, ) sess.run(eval_summary_writer_flush_op) start_time = time.time() sess.run(collect_op) collect_time += time.time() - start_time # ################################################ # # -------- Prediction-Implementation Start ------- # # ################################################ # if statePred or actionPred: #get trajectory and clear Replay-Buffer collectedTrajectory = sess.run(gather_op) if curiosity: #experimental sess.run(clear_op) #augment reward in trajectory collectedTrajectory = state_pred_net.augmentReward( collectedTrajectory) #write augmented trajectory back into replay buffer for j in range(len(collectedTrajectory[0][0])): i = j - 1 sess.run( add_batch_op, feed_dict={ step_type: collectedTrajectory[0][:, i], state: collectedTrajectory[1].get("state")[:, i, :], info: collectedTrajectory[1].get("info")[:, i], mask: collectedTrajectory[1].get("mask")[:, i, :], state2: collectedTrajectory[1].get("state2")[:, i, :], action: collectedTrajectory[2][:, i], logits: collectedTrajectory[3].get("logits")[:, i, :], next_step_type: collectedTrajectory[4][:, i], reward: collectedTrajectory[5][:, i], discount: collectedTrajectory[6][:, i] }) #train prediction network if statePred: state_pred_net.train(collectedTrajectory, True) if actionPred: action_pred_net.train(collectedTrajectory, True) # ################################################ # # ------ Prediction-Implementation Stop ---------- # # ################################################ # train_time = 0 total_loss = -1 #indicates that there was no training if train: start_time = time.time() total_loss, _ = sess.run([train_op, summary_ops]) train_time += time.time() - start_time # ################################################ # # ---------- Logging and Checkpointing ----------- # # ################################################ # if saveModel: global_step_val = sess.run(global_step) if global_step_val % log_interval == 0: logging.info('step = %d, loss = %f', global_step_val, total_loss) steps_per_sec = ((global_step_val - timed_at_step) / (collect_time + train_time)) logging.info('%.3f steps/sec', steps_per_sec) sess.run( steps_per_second_summary, feed_dict={steps_per_second_ph: steps_per_sec}) logging.info( '%s', 'collect_time = {}, train_time = {}'.format( collect_time, train_time)) timed_at_step = global_step_val collect_time = 0 train_time = 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 saveModel: # One final eval before exiting. 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, ) sess.run(eval_summary_writer_flush_op) tf.reset_default_graph()