def _restore_checkpoint(self):
global_step = get_global_counter()
checkpointer = tfa_common.Checkpointer(
ckpt_dir=os.path.join(self._train_dir, 'algorithm'),
algorithm=self._algorithm,
metrics=metric_utils.MetricsGroup(self._driver.get_metrics(),
'metrics'),
global_step=global_step)
checkpointer.initialize_or_restore()
self._checkpointer = checkpointer
def create_checkpoints(agent,
global_step,
checkpoint_dir,
train_metrics,
eval_metrics,
max_to_keep=2):
train_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(checkpoint_dir, 'collect_policy'),
max_to_keep=max_to_keep,
agent=agent,
policy=agent.collect_policy,
global_step=global_step,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(checkpoint_dir, 'policy'),
max_to_keep=max_to_keep,
agent=agent,
policy=agent.policy,
global_step=global_step,
metrics=metric_utils.MetricsGroup(eval_metrics, 'eval_metrics'))
return train_checkpointer, policy_checkpointer
def _create_checkpointer(ckpt_dir, member, ckpt='train'):
if ckpt == 'train':
return common.Checkpointer(
ckpt_dir=ckpt_dir,
agent=member.agent,
global_step=member.step_metrics[FP.IDX_ENV_STEPS],
metrics=metric_utils.MetricsGroup(
member.step_metrics + member.train_metrics,
'train_metrics'))
elif ckpt == 'policy':
return common.Checkpointer(
ckpt_dir=os.path.join(ckpt_dir, 'policy'),
policy=member.agent.policy,
global_step=member.step_metrics[FP.IDX_ENV_STEPS])
else:
raise ValueError
def train(
root_dir,
load_root_dir=None,
env_load_fn=None,
env_name=None,
num_parallel_environments=1, # pylint: disable=unused-argument
agent_class=None,
initial_collect_random=True, # pylint: disable=unused-argument
initial_collect_driver_class=None,
collect_driver_class=None,
num_global_steps=1000000,
train_steps_per_iteration=1,
train_metrics=None,
# Safety Critic training args
train_sc_steps=10,
train_sc_interval=300,
online_critic=False,
# Params for eval
run_eval=False,
num_eval_episodes=30,
eval_interval=1000,
eval_metrics_callback=None,
# Params for summaries and logging
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
rb_checkpoint_interval=20000,
keep_rb_checkpoint=False,
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
early_termination_fn=None,
env_metric_factories=None): # pylint: disable=unused-argument
"""A simple train and eval for SC-SAC."""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
train_metrics = train_metrics or []
if run_eval:
eval_dir = os.path.join(root_dir, 'eval')
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes),
] + [tf_py_metric.TFPyMetric(m) for m in train_metrics]
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 = env_load_fn(env_name)
if not isinstance(tf_env, tf_py_environment.TFPyEnvironment):
tf_env = tf_py_environment.TFPyEnvironment(tf_env)
if run_eval:
eval_py_env = env_load_fn(env_name)
eval_tf_env = tf_py_environment.TFPyEnvironment(eval_py_env)
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
print('obs spec:', observation_spec)
print('action spec:', action_spec)
if online_critic:
resample_metric = tf_py_metric.TfPyMetric(
py_metrics.CounterMetric('unsafe_ac_samples'))
tf_agent = agent_class(time_step_spec,
action_spec,
train_step_counter=global_step,
resample_metric=resample_metric)
else:
tf_agent = agent_class(time_step_spec,
action_spec,
train_step_counter=global_step)
tf_agent.initialize()
# Make the replay buffer.
collect_data_spec = tf_agent.collect_data_spec
logging.info('Allocating replay buffer ...')
# Add to replay buffer and other agent specific observers.
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
collect_data_spec, max_length=1000000)
logging.info('RB capacity: %i', replay_buffer.capacity)
logging.info('ReplayBuffer Collect data spec: %s', collect_data_spec)
agent_observers = [replay_buffer.add_batch]
if online_critic:
online_replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
collect_data_spec, max_length=10000)
online_rb_ckpt_dir = os.path.join(train_dir,
'online_replay_buffer')
online_rb_checkpointer = common.Checkpointer(
ckpt_dir=online_rb_ckpt_dir,
max_to_keep=1,
replay_buffer=online_replay_buffer)
clear_rb = common.function(online_replay_buffer.clear)
agent_observers.append(online_replay_buffer.add_batch)
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size),
] + [tf_py_metric.TFPyMetric(m) for m in train_metrics]
if not online_critic:
eval_policy = tf_agent.policy
else:
eval_policy = tf_agent._safe_policy # pylint: disable=protected-access
initial_collect_policy = random_tf_policy.RandomTFPolicy(
time_step_spec, action_spec)
if not online_critic:
collect_policy = tf_agent.collect_policy
else:
collect_policy = tf_agent._safe_policy # pylint: disable=protected-access
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=eval_policy,
global_step=global_step)
safety_critic_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, 'safety_critic'),
safety_critic=tf_agent._safety_critic_network, # pylint: disable=protected-access
global_step=global_step)
rb_ckpt_dir = os.path.join(train_dir, 'replay_buffer')
rb_checkpointer = common.Checkpointer(ckpt_dir=rb_ckpt_dir,
max_to_keep=1,
replay_buffer=replay_buffer)
if load_root_dir:
load_root_dir = os.path.expanduser(load_root_dir)
load_train_dir = os.path.join(load_root_dir, 'train')
misc.load_pi_ckpt(load_train_dir, tf_agent) # loads tf_agent
if load_root_dir is None:
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
safety_critic_checkpointer.initialize_or_restore()
collect_driver = collect_driver_class(tf_env,
collect_policy,
observers=agent_observers +
train_metrics)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
if not rb_checkpointer.checkpoint_exists:
logging.info('Performing initial collection ...')
common.function(
initial_collect_driver_class(tf_env,
initial_collect_policy,
observers=agent_observers +
train_metrics).run)()
last_id = replay_buffer._get_last_id() # pylint: disable=protected-access
logging.info('Data saved after initial collection: %d steps',
last_id)
tf.print(
replay_buffer._get_rows_for_id(last_id), # pylint: disable=protected-access
output_stream=logging.info)
if run_eval:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
if FLAGS.viz_pm:
eval_fig_dir = osp.join(eval_dir, 'figs')
if not tf.io.gfile.isdir(eval_fig_dir):
tf.io.gfile.makedirs(eval_fig_dir)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
num_steps=2).prefetch(3)
iterator = iter(dataset)
if online_critic:
online_dataset = online_replay_buffer.as_dataset(
num_parallel_calls=3, num_steps=2).prefetch(3)
online_iterator = iter(online_dataset)
@common.function
def critic_train_step():
"""Builds critic training step."""
experience, buf_info = next(online_iterator)
if env_name in [
'IndianWell', 'IndianWell2', 'IndianWell3',
'DrunkSpider', 'DrunkSpiderShort'
]:
safe_rew = experience.observation['task_agn_rew']
else:
safe_rew = agents.process_replay_buffer(
online_replay_buffer, as_tensor=True)
safe_rew = tf.gather(safe_rew,
tf.squeeze(buf_info.ids),
axis=1)
ret = tf_agent.train_sc(experience, safe_rew)
clear_rb()
return ret
@common.function
def train_step():
experience, _ = next(iterator)
ret = tf_agent.train(experience)
return ret
if not early_termination_fn:
early_termination_fn = lambda: False
loss_diverged = False
# How many consecutive steps was loss diverged for.
loss_divergence_counter = 0
mean_train_loss = tf.keras.metrics.Mean(name='mean_train_loss')
if online_critic:
mean_resample_ac = tf.keras.metrics.Mean(
name='mean_unsafe_ac_samples')
resample_metric.reset()
while (global_step.numpy() <= num_global_steps
and not early_termination_fn()):
# Collect and train.
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
if online_critic:
mean_resample_ac(resample_metric.result())
resample_metric.reset()
if time_step.is_last():
resample_ac_freq = mean_resample_ac.result()
mean_resample_ac.reset_states()
tf.compat.v2.summary.scalar(name='unsafe_ac_samples',
data=resample_ac_freq,
step=global_step)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
mean_train_loss(train_loss.loss)
if online_critic:
if global_step.numpy() % train_sc_interval == 0:
for _ in range(train_sc_steps):
sc_loss, lambda_loss = critic_train_step() # pylint: disable=unused-variable
total_loss = mean_train_loss.result()
mean_train_loss.reset_states()
# Check for exploding losses.
if (math.isnan(total_loss) or math.isinf(total_loss)
or total_loss > MAX_LOSS):
loss_divergence_counter += 1
if loss_divergence_counter > TERMINATE_AFTER_DIVERGED_LOSS_STEPS:
loss_diverged = True
break
else:
loss_divergence_counter = 0
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('step = %d, loss = %f', global_step.numpy(),
total_loss)
steps_per_sec = (global_step.numpy() -
timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
global_step_val = global_step.numpy()
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)
safety_critic_checkpointer.save(global_step=global_step_val)
if global_step_val % rb_checkpoint_interval == 0:
if online_critic:
online_rb_checkpointer.save(global_step=global_step_val)
rb_checkpointer.save(global_step=global_step_val)
if run_eval and global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
if FLAGS.viz_pm:
savepath = 'step{}.png'.format(global_step_val)
savepath = osp.join(eval_fig_dir, savepath)
misc.record_episode_vis_summary(eval_tf_env, eval_policy,
savepath)
if not keep_rb_checkpoint:
misc.cleanup_checkpoints(rb_ckpt_dir)
if loss_diverged:
# Raise an error at the very end after the cleanup.
raise ValueError('Loss diverged to {} at step {}, terminating.'.format(
total_loss, global_step.numpy()))
return total_loss
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
env_load_fn=suite_mujoco.load,
random_seed=None,
# TODO(b/127576522): rename to policy_fc_layers.
actor_fc_layers=(200, 100),
value_fc_layers=(200, 100),
use_rnns=False,
lstm_size=(20, ),
# Params for collect
num_environment_steps=25000000,
collect_episodes_per_iteration=30,
num_parallel_environments=30,
replay_buffer_capacity=1001, # Per-environment
# Params for train
num_epochs=25,
learning_rate=1e-3,
# Params for eval
num_eval_episodes=30,
eval_interval=500,
# Params for summaries and logging
train_checkpoint_interval=500,
policy_checkpoint_interval=500,
log_interval=50,
summary_interval=50,
summaries_flush_secs=1,
use_tf_functions=True,
debug_summaries=False,
summarize_grads_and_vars=False):
"""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')
saved_model_dir = os.path.join(root_dir, 'policy_saved_model')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
if random_seed is not None:
tf.compat.v1.set_random_seed(random_seed)
eval_tf_env = tf_py_environment.TFPyEnvironment(env_load_fn(env_name))
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,
lstm_size=lstm_size)
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,
activation_fn=tf.keras.activations.tanh)
value_net = value_network.ValueNetwork(
tf_env.observation_spec(),
fc_layer_params=value_fc_layers,
activation_fn=tf.keras.activations.tanh)
tf_agent = ppo_clip_agent.PPOClipAgent(
tf_env.time_step_spec(),
tf_env.action_spec(),
optimizer,
actor_net=actor_net,
value_net=value_net,
entropy_regularization=0.0,
importance_ratio_clipping=0.2,
normalize_observations=False,
normalize_rewards=False,
use_gae=True,
num_epochs=num_epochs,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step)
tf_agent.initialize()
environment_steps_metric = tf_metrics.EnvironmentSteps()
step_metrics = [
tf_metrics.NumberOfEpisodes(),
environment_steps_metric,
]
train_metrics = step_metrics + [
tf_metrics.AverageReturnMetric(
batch_size=num_parallel_environments),
tf_metrics.AverageEpisodeLengthMetric(
batch_size=num_parallel_environments),
]
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
tf_agent.collect_data_spec,
batch_size=num_parallel_environments,
max_length=replay_buffer_capacity)
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=eval_policy,
global_step=global_step)
saved_model = policy_saver.PolicySaver(eval_policy,
train_step=global_step)
train_checkpointer.initialize_or_restore()
collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_episodes=collect_episodes_per_iteration)
def train_step():
trajectories = replay_buffer.gather_all()
return tf_agent.train(experience=trajectories)
if use_tf_functions:
# TODO(b/123828980): Enable once the cause for slowdown was identified.
collect_driver.run = common.function(collect_driver.run,
autograph=False)
tf_agent.train = common.function(tf_agent.train, autograph=False)
train_step = common.function(train_step)
collect_time = 0
train_time = 0
timed_at_step = global_step.numpy()
while environment_steps_metric.result() < num_environment_steps:
global_step_val = global_step.numpy()
if global_step_val % eval_interval == 0:
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
start_time = time.time()
collect_driver.run()
collect_time += time.time() - start_time
start_time = time.time()
total_loss, _ = train_step()
replay_buffer.clear()
train_time += time.time() - start_time
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=step_metrics)
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)
logging.info('collect_time = %.3f, train_time = %.3f',
collect_time, train_time)
with tf.compat.v2.summary.record_if(True):
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
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)
saved_model_path = os.path.join(
saved_model_dir,
'policy_' + ('%d' % global_step_val).zfill(9))
saved_model.save(saved_model_path)
timed_at_step = global_step_val
collect_time = 0
train_time = 0
# One final eval before exiting.
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
def load_agents_and_create_videos(root_dir,
env_name='CartPole-v0',
num_iterations=NUM_ITERATIONS,
max_ep_steps=1000,
train_sequence_length=1,
# Params for QNetwork
fc_layer_params=((128,64,32)),
# Params for QRnnNetwork
input_fc_layer_params=(50,),
lstm_size=(20,),
output_fc_layer_params=(20,),
# Params for collect
initial_collect_steps=1000,
collect_steps_per_iteration=1,
epsilon_greedy=0.1,
replay_buffer_capacity=10000,
# 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,
use_tf_functions=True,
# Params for eval
num_eval_episodes=10,
num_random_episodes=1,
eval_interval=1000,
# Params for checkpoints
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
rb_checkpoint_interval=20000,
# Params for summaries and logging
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
eval_metrics_callback=None,
random_metrics_callback=None):
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
random_dir = os.path.join(root_dir, 'random')
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)]
global_step = tf.compat.v1.train.get_or_create_global_step()
# Match the environments used in training
tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env_name, max_episode_steps=max_ep_steps))
eval_py_env = suite_gym.load(env_name, max_episode_steps=max_ep_steps)
eval_tf_env = tf_py_environment.TFPyEnvironment(eval_py_env)
if train_sequence_length != 1 and n_step_update != 1:
raise NotImplementedError(
'train_eval does not currently support n-step updates with stateful '
'networks (i.e., RNNs)')
if train_sequence_length > 1:
q_net = q_rnn_network.QRnnNetwork(
tf_env.observation_spec(),
tf_env.action_spec(),
input_fc_layer_params=input_fc_layer_params,
lstm_size=lstm_size,
output_fc_layer_params=output_fc_layer_params)
else:
q_net = q_network.QNetwork(
tf_env.observation_spec(),
tf_env.action_spec(),
fc_layer_params=fc_layer_params)
train_sequence_length = n_step_update
# Match the agents used in training
tf_agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.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=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)
tf_agent.initialize()
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),]
eval_policy = tf_agent.policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
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=eval_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)
# Load the data from training
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
# Define a random policy for comparison
random_policy = random_tf_policy.RandomTFPolicy(eval_tf_env.time_step_spec(),
eval_tf_env.action_spec())
# Make movies of the trained agent and a random agent
date_string = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S')
trained_filename = "trained-agent" + date_string
create_policy_eval_video(eval_tf_env, eval_py_env, tf_agent.policy, trained_filename)
random_filename = 'random-agent ' + date_string
create_policy_eval_video(eval_tf_env, eval_py_env, random_policy, random_filename)
def train_eval(
root_dir,
env_name='CartPole-v0',
num_iterations=100000,
train_sequence_length=1,
# Params for QNetwork
fc_layer_params=(100, ),
# Params for QRnnNetwork
input_fc_layer_params=(50, ),
lstm_size=(20, ),
output_fc_layer_params=(20, ),
# 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,
use_tf_functions=True,
# Params for eval
num_eval_episodes=10,
eval_interval=1000,
# Params for checkpoints
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
rb_checkpoint_interval=20000,
# Params for summaries and logging
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 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 = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env_name))
eval_tf_env = tf_py_environment.TFPyEnvironment(
suite_gym.load(env_name))
if train_sequence_length != 1 and n_step_update != 1:
raise NotImplementedError(
'train_eval does not currently support n-step updates with stateful '
'networks (i.e., RNNs)')
if train_sequence_length > 1:
q_net = q_rnn_network.QRnnNetwork(
tf_env.observation_spec(),
tf_env.action_spec(),
input_fc_layer_params=input_fc_layer_params,
lstm_size=lstm_size,
output_fc_layer_params=output_fc_layer_params)
else:
q_net = q_network.QNetwork(tf_env.observation_spec(),
tf_env.action_spec(),
fc_layer_params=fc_layer_params)
train_sequence_length = n_step_update
# TODO(b/127301657): Decay epsilon based on global step, cf. cl/188907839
tf_agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.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=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)
tf_agent.initialize()
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),
]
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=collect_steps_per_iteration)
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=eval_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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
if use_tf_functions:
# To speed up collect use common.function.
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
# Collect initial replay data.
logging.info(
'Initializing replay buffer by collecting experience for %d steps with '
'a random policy.', initial_collect_steps)
dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=initial_collect_steps).run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=train_sequence_length +
1).prefetch(3)
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('step = %d, loss = %f', global_step.numpy(),
train_loss.loss)
steps_per_sec = (global_step.numpy() -
timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
if global_step.numpy() % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
return train_loss
def __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 run():
tf_env = tf_py_environment.TFPyEnvironment(SnakeEnv())
eval_env = tf_py_environment.TFPyEnvironment(SnakeEnv(step_limit=50))
q_net = q_network.QNetwork(
tf_env.observation_spec(),
tf_env.action_spec(),
conv_layer_params=(),
fc_layer_params=(512, 256, 128),
)
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
global_counter = tf.compat.v1.train.get_or_create_global_step()
agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=global_counter,
gamma=0.95,
epsilon_greedy=0.1,
n_step_update=1,
)
root_dir = os.path.join('/tf-logs', 'snake')
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
agent.initialize()
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),
]
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_max_length,
)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
agent.collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=collect_steps_per_iteration,
)
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=agent,
global_step=global_counter,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'),
)
policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, 'policy'),
policy=agent.policy,
global_step=global_counter,
)
rb_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer,
)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
collect_driver.run = common.function(collect_driver.run)
agent.train = common.function(agent.train)
random_policy = random_tf_policy.RandomTFPolicy(tf_env.time_step_spec(),
tf_env.action_spec())
if replay_buffer.num_frames() >= initial_collect_steps:
logging.info("We loaded memories, not doing random seed")
else:
logging.info("Capturing %d steps to seed with random memories",
initial_collect_steps)
dynamic_step_driver.DynamicStepDriver(
tf_env,
random_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=initial_collect_steps).run()
train_summary_writer = tf.summary.create_file_writer(train_dir)
train_summary_writer.set_as_default()
avg_returns = []
avg_return_metric = tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes)
eval_metrics = [
avg_return_metric,
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),
]
logging.info("Running initial evaluation")
results = metric_utils.eager_compute(
eval_metrics,
eval_env,
agent.policy,
num_episodes=num_eval_episodes,
train_step=global_counter,
summary_writer=tf.summary.create_file_writer(eval_dir),
summary_prefix='Metrics',
)
avg_returns.append(
(global_counter.numpy(), avg_return_metric.result().numpy()))
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = agent.collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_counter.numpy()
time_acc = 0
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=2).prefetch(3)
iterator = iter(dataset)
@common.function
def train_step():
experience, _ = next(iterator)
return agent.train(experience)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
step = global_counter.numpy()
if step % log_interval == 0:
logging.info("step = %d, loss = %f", step, train_loss.loss)
steps_per_sec = (step - timed_at_step) / time_acc
logging.info("%.3f steps/sec", steps_per_sec)
timed_at_step = step
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_counter,
step_metrics=train_metrics[:2])
if step % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=step)
if step % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=step)
if step % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=step)
if step % capture_interval == 0:
print("Capturing run:")
capture_run(os.path.join(root_dir, "snake" + str(step) + ".mp4"),
eval_env, agent.policy)
if step % eval_interval == 0:
print("EVALUTION TIME:")
results = metric_utils.eager_compute(
eval_metrics,
eval_env,
agent.policy,
num_episodes=num_eval_episodes,
train_step=global_counter,
summary_writer=tf.summary.create_file_writer(eval_dir),
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
avg_returns.append(
(global_counter.numpy(), avg_return_metric.result().numpy()))
def train_eval(
root_dir,
env_name='CartPole-v0',
num_iterations=5e5,
train_sequence_length=1,
# Params for QNetwork
fc_layer_params=(
64,
64,
),
# Params for QRnnNetwork
input_fc_layer_params=(50, ),
lstm_size=(6, ),
output_fc_layer_params=(30, ),
# Params for collect
initial_collect_steps=2000,
collect_steps_per_iteration=6,
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=6,
batch_size=32,
learning_rate=1e-3,
n_step_update=1,
gamma=0.99,
reward_scale_factor=1.0,
gradient_clipping=None,
use_tf_functions=True,
# Params for eval
num_eval_episodes=1,
eval_interval=1000,
# Params for checkpoints
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
rb_checkpoint_interval=20000,
# Params for summaries and logging
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 DQN."""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
clusters = pickle.load(open('clusters.pickle', 'rb'))
graph = nx.read_gpickle('graph.gpickle')
print(graph.nodes)
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
tf_env = tf_py_environment.TFPyEnvironment(
suite_gym.load(env_name,
gym_kwargs={
'graph': graph,
'clusters': clusters
}))
eval_tf_env = tf_py_environment.TFPyEnvironment(
suite_gym.load(env_name,
gym_kwargs={
'graph': graph,
'clusters': clusters
}))
if train_sequence_length != 1 and n_step_update != 1:
raise NotImplementedError(
'train_eval does not currently support n-step updates with stateful '
'networks (i.e., RNNs)')
action_spec = tf_env.action_spec()
num_actions = action_spec.maximum - action_spec.minimum + 1
if train_sequence_length > 1:
q_net = create_recurrent_network(input_fc_layer_params, lstm_size,
output_fc_layer_params,
num_actions)
else:
q_net = create_feedforward_network(fc_layer_params, num_actions)
train_sequence_length = n_step_update
q_net = GATNetwork(tf_env.observation_spec(), tf_env.action_spec(),
graph)
#time_step = tf_env.reset()
#q_net(time_step.observation, time_step.step_type)
#q_net = actor_distribution_network.ActorDistributionNetwork(
# tf_env.observation_spec(),
# tf_env.action_spec(),
# fc_layer_params=fc_layer_params)
#q_net = QNetwork(tf_env.observation_spec(), tf_env.action_spec(), 30)
# TODO(b/127301657): Decay epsilon based on global step, cf. cl/188907839
tf_agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.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=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)
#critic_net = ddpg.critic_network.CriticNetwork(
#(tf_env.observation_spec(), tf_env.action_spec()),
#observation_fc_layer_params=None,
#action_fc_layer_params=None,
#joint_fc_layer_params=(64,64,),
#kernel_initializer='glorot_uniform',
#last_kernel_initializer='glorot_uniform')
#tf_agent = DdpgAgent(tf_env.time_step_spec(),
# tf_env.action_spec(),
# actor_network=q_net,
# critic_network=critic_net,
# actor_optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate),
# critic_optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate),
# ou_stddev=0.0,
# ou_damping=0.0)
tf_agent.initialize()
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),
tf_metrics.MaxReturnMetric(),
]
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=collect_steps_per_iteration)
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=eval_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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
if use_tf_functions:
# To speed up collect use common.function.
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
# Collect initial replay data.
logging.info(
'Initializing replay buffer by collecting experience for %d steps with '
'a random policy.', initial_collect_steps)
dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=initial_collect_steps).run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=train_sequence_length +
1).prefetch(3)
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('step = %d, loss = %f', global_step.numpy(),
train_loss.loss)
steps_per_sec = (global_step.numpy() -
timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
if global_step.numpy() % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
print(tf_env.envs[0]._gym_env.best_controllers)
print(tf_env.envs[0]._gym_env.best_reward)
tf_env.envs[0]._gym_env.reset()
centroid_controllers, heuristic_distance = tf_env.envs[
0]._gym_env.graphCentroidAction()
# Convert heuristic controllers to actual
print(centroid_controllers)
# Assume all clusters same length
#centroid_controllers.sort()
#cluster_len = len(clusters[0])
#for i in range(len(clusters)):
# centroid_controllers[i] -= i * cluster_len
print(centroid_controllers)
for cont in centroid_controllers:
(_, reward_final, _, _) = tf_env.envs[0]._gym_env.step(cont)
best_heuristic = reward_final
print(tf_env.envs[0]._gym_env.controllers, reward_final)
return train_loss
def train_eval(
root_dir,
env_name='Blob2d-v1',
num_iterations=100000,
train_sequence_length=1,
collect_steps_per_iteration=1,
initial_collect_steps=1500,
replay_buffer_max_length=10000,
batch_size=64,
learning_rate=1e-3,
num_eval_episodes=10,
eval_interval=1000,
# Params for QNetwork
fc_layer_params=(100, ),
use_tf_functions=False,
## train params
train_steps_per_iteration=1,
train_checkpoint_interval=1000,
policy_checkpoint_interval=1000,
rb_checkpoint_interval=1000,
n_step_update=1,
## Params for Summaries and logging
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
eval_metrics_callback=None):
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
tf_env = tf_py_environment.TFPyEnvironment(suite_gym.load(env_name))
eval_tf_env = tf_py_environment.TFPyEnvironment(
suite_gym.load(env_name))
if train_sequence_length != 1 and n_step_update != 1:
raise NotImplementedError(
'train_eval does not currently support n-step updates with stateful '
'networks (i.e., RNNs)')
env = suite_gym.load('Blob2d-v1')
tf_env = tf_py_environment.TFPyEnvironment(env)
action_spec = tf_env.action_spec()
fc_layer_params = (100, )
q_net = q_network.QNetwork(tf_env.observation_spec(),
tf_env.action_spec(),
fc_layer_params=fc_layer_params)
agent = dqn_agent.DqnAgent(
tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate),
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=global_step)
agent.initialize()
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),
]
eval_policy = agent.policy
collect_policy = agent.collect_policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_max_length)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=collect_steps_per_iteration)
train_checkpointer = common.Checkpointer(ckpt_dir=train_dir,
agent=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=eval_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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
logging.info(
'Initializing replay buffer by collecting experience for %d steps with '
'a random policy.', initial_collect_steps)
dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=initial_collect_steps).run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=train_sequence_length +
1).prefetch(3)
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
# Main Training loop.
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('step = %d, loss = %f', global_step.numpy(),
train_loss.loss)
steps_per_sec = (global_step.numpy() - timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
if global_step.numpy() % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
return train_loss
def train_eval(
root_dir,
env_name='sawyer_reach',
num_iterations=3000000,
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,
gamma=0.99,
gradient_clipping=None,
use_tf_functions=True,
# Params for eval
num_eval_episodes=30,
eval_interval=10000,
# Params for summaries and logging
train_checkpoint_interval=200000,
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
random_seed=0,
max_future_steps=50,
actor_std=None,
log_subset=None,
):
"""A simple train and eval for SAC."""
np.random.seed(random_seed)
tf.random.set_seed(random_seed)
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()
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, eval_tf_env, obs_dim = c_learning_envs.load(env_name)
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
if actor_std is None:
proj_net = tanh_normal_projection_network.TanhNormalProjectionNetwork
else:
proj_net = functools.partial(
tanh_normal_projection_network.TanhNormalProjectionNetwork,
std_transform=lambda t: actor_std * tf.ones_like(t))
actor_net = actor_distribution_network.ActorDistributionNetwork(
observation_spec,
action_spec,
fc_layer_params=actor_fc_layers,
continuous_projection_net=proj_net)
critic_net = c_learning_utils.ClassifierCriticNetwork(
(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,
kernel_initializer='glorot_uniform',
last_kernel_initializer='glorot_uniform')
tf_agent = c_learning_agent.CLearningAgent(
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),
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=bce_loss,
gamma=gamma,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step)
tf_agent.initialize()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),
c_learning_utils.FinalDistance(
buffer_size=num_eval_episodes, obs_dim=obs_dim),
c_learning_utils.MinimumDistance(
buffer_size=num_eval_episodes, obs_dim=obs_dim),
c_learning_utils.DeltaDistance(
buffer_size=num_eval_episodes, obs_dim=obs_dim),
]
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size),
c_learning_utils.InitialDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
obs_dim=obs_dim),
c_learning_utils.FinalDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
obs_dim=obs_dim),
c_learning_utils.MinimumDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
obs_dim=obs_dim),
c_learning_utils.DeltaDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
obs_dim=obs_dim),
]
if log_subset is not None:
start_index, end_index = log_subset
for name, metrics in [('train', train_metrics), ('eval', eval_metrics)]:
metrics.extend([
c_learning_utils.InitialDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size if name == 'train' else 10,
obs_dim=obs_dim,
start_index=start_index,
end_index=end_index,
name='SubsetInitialDistance'),
c_learning_utils.FinalDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size if name == 'train' else 10,
obs_dim=obs_dim,
start_index=start_index,
end_index=end_index,
name='SubsetFinalDistance'),
c_learning_utils.MinimumDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size if name == 'train' else 10,
obs_dim=obs_dim,
start_index=start_index,
end_index=end_index,
name='SubsetMinimumDistance'),
c_learning_utils.DeltaDistance(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size if name == 'train' else 10,
obs_dim=obs_dim,
start_index=start_index,
end_index=end_index,
name='SubsetDeltaDistance'),
])
eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
collect_policy = tf_agent.collect_policy
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=None)
train_checkpointer.initialize_or_restore()
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
replay_observer = [replay_buffer.add_batch]
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=initial_collect_steps)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=collect_steps_per_iteration)
if use_tf_functions:
initial_collect_driver.run = common.function(initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
# Save the hyperparameters
operative_filename = os.path.join(root_dir, 'operative.gin')
with tf.compat.v1.gfile.Open(operative_filename, 'w') as f:
f.write(gin.operative_config_str())
logging.info(gin.operative_config_str())
if replay_buffer.num_frames() == 0:
# Collect initial replay data.
logging.info(
'Initializing replay buffer by collecting experience for %d steps '
'with a random policy.', initial_collect_steps)
initial_collect_driver.run()
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
def _filter_invalid_transition(trajectories, unused_arg1):
return ~trajectories.is_boundary()[0]
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=max_future_steps)
dataset = dataset.unbatch().filter(_filter_invalid_transition)
dataset = dataset.batch(batch_size, drop_remainder=True)
goal_fn = functools.partial(
c_learning_utils.goal_fn,
batch_size=batch_size,
obs_dim=obs_dim,
gamma=gamma)
dataset = dataset.map(goal_fn)
dataset = dataset.prefetch(5)
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
global_step_val = global_step.numpy()
while global_step_val < num_iterations:
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
global_step_val = global_step.numpy()
if global_step_val % log_interval == 0:
logging.info('step = %d, loss = %f', global_step_val,
train_loss.loss)
steps_per_sec = (global_step_val - timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(
name='global_steps_per_sec', data=steps_per_sec, step=global_step)
timed_at_step = global_step_val
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(
train_step=global_step, step_metrics=train_metrics[:2])
if global_step_val % eval_interval == 0:
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
if global_step_val % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step_val)
return train_loss
def train_eval(root_dir, tf_env, eval_tf_env, agent, num_iterations,
initial_collect_steps, collect_steps_per_iteration,
replay_buffer_capacity, train_steps_per_iteration, batch_size,
use_tf_functions, num_eval_episodes, eval_interval,
train_checkpoint_interval, policy_checkpoint_interval,
rb_checkpoint_interval, log_interval, summary_interval,
summaries_flush_secs):
"""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 = [
#tf_metrics.ChosenActionHistogram(buffer_size=num_eval_episodes),
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
#tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
tf_env = tf_env
eval_tf_env = eval_tf_env
tf_agent = agent
train_metrics = [
#tf_metrics.ChosenActionHistogram(),
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(buffer_size=1),
#tf_metrics.AverageEpisodeLengthMetric(),
]
diverged = False
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=collect_steps_per_iteration)
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=tf_agent,
global_step=global_step,
max_to_keep=1,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
train_dir, 'policy'),
policy=eval_policy,
max_to_keep=1,
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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
best_policy = -1000
if use_tf_functions:
# To speed up collect use common.function.
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
#Collect initial replay data.
dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_steps=initial_collect_steps).run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=2).prefetch(3)
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
if np.isnan(train_loss.loss).any():
diverged = True
break
elif np.isinf(train_loss.loss).any():
diverged = True
break
if global_step.numpy() % log_interval == 0:
print('step = {0}, loss = {1}'.format(global_step.numpy(),
train_loss.loss))
steps_per_sec = (global_step.numpy() -
timed_at_step) / time_acc
print('{0} steps/sec'.format(steps_per_sec))
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
if global_step.numpy() % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step.numpy())
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if results["AverageReturn"].numpy() > best_policy:
print("New best policy found")
print(results["AverageReturn"].numpy())
best_policy = results["AverageReturn"].numpy()
policy_checkpointer.save(global_step=global_step.numpy())
metric_utils.log_metrics(eval_metrics)
return train_loss
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 __init__(self):
self._train_py_env = suite_gym.load(T48GymEnv.GYM_ENV_NAME, max_episode_steps=T48GymTensorflowContext.max_episode_steps)
self._eval_py_env = suite_gym.load(T48GymEnv.GYM_ENV_NAME, max_episode_steps=T48GymTensorflowContext.max_episode_steps)
self._train_env = tf_py_environment.TFPyEnvironment(self._train_py_env)
self._eval_env = tf_py_environment.TFPyEnvironment(self._eval_py_env)
self._global_step = tf.compat.v1.train.get_or_create_global_step()
self._q_net = q_network.QNetwork(
self._train_env.observation_spec(),
self._train_env.action_spec(),
fc_layer_params=(100,))
self._agent = dqn_agent.DdqnAgent(
self._train_env.time_step_spec(),
self._train_env.action_spec(),
q_network=self._q_net,
optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=T48GymTensorflowContext.learning_rate),
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=self._global_step,
epsilon_greedy=0.0)
self._agent.initialize()
self._agent.train = common.function(self._agent.train)
self._replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=self._agent.collect_data_spec,
batch_size=self._train_env.batch_size,
max_length=T48GymTensorflowContext.replay_buffer_max_length)
self._dataset = self._replay_buffer.as_dataset(
num_parallel_calls=3,
sample_batch_size=self._train_env.batch_size,
num_steps=2).prefetch(3)
self._agent.initialize()
self._iterator = iter(self._dataset)
self._RANDOM_POLICY = random_tf_policy.RandomTFPolicy(self._train_env.time_step_spec(),
self._train_env.action_spec())
self._collect_policy = self._agent.collect_policy
self._eval_policy = self._agent.policy
self._collect_driver = dynamic_step_driver.DynamicStepDriver(
self._train_env,
self._collect_policy,
observers=[self._replay_buffer.add_batch] + T48GymTensorflowContext.train_metrics,
num_steps=2)
self._train_checkpointer = common.Checkpointer(
ckpt_dir=T48GymTensorflowContext.train_dir,
global_step=self._global_step,
agent=self._agent,
metrics=metric_utils.MetricsGroup(T48GymTensorflowContext.train_metrics, 'train_metrics'))
self._policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(T48GymTensorflowContext.train_dir, 'policy'),
global_step=self._global_step,
policy=self._eval_policy)
self._rb_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(T48GymTensorflowContext.train_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=self._replay_buffer)
self._tf_policy_saver = policy_saver.PolicySaver(self._agent.policy)
self._train_checkpointer.initialize_or_restore()
self._policy_checkpointer.initialize_or_restore()
self._rb_checkpointer.initialize_or_restore()
def train(self,
training_iterations=TRAINING_ITERATIONS,
training_stock_list=None):
self.reset(training_stock_list)
train_dir = 'training_data_progress/train-' + self.name
eval_dir = 'training_data_progress/eval-' + self.name
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=self.tf_agent.collect_data_spec,
batch_size=self.tf_training_env.batch_size,
max_length=MAX_BUFFER_SIZE)
summaries_flush_secs = 10
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=NUM_EVAL_EPISODES),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=NUM_EVAL_EPISODES)
]
global_step = self.tf_agent.train_step_counter
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % LOG_INTERVAL, 0)):
replay_observer = [replay_buffer.add_batch]
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(
buffer_size=NUM_EVAL_EPISODES,
batch_size=self.tf_training_env.batch_size),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=NUM_EVAL_EPISODES,
batch_size=self.tf_training_env.batch_size),
]
eval_policy = greedy_policy.GreedyPolicy(self.tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
self.tf_training_env.time_step_spec(),
self.tf_training_env.action_spec())
collect_policy = self.tf_agent.collect_policy
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=self.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=eval_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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
initial_collect_driver_random = dynamic_step_driver.DynamicStepDriver(
self.tf_training_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=INIT_COLLECT_STEPS)
initial_collect_driver_random.run = common.function(
initial_collect_driver_random.run)
collect_driver = dynamic_step_driver.DynamicStepDriver(
self.tf_training_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=STEP_ITERATIONS)
collect_driver.run = common.function(collect_driver.run)
self.tf_agent.train = common.function(self.tf_agent.train)
# Collect some initial data.
# Random
random_policy = random_tf_policy.RandomTFPolicy(
self.tf_training_env.time_step_spec(),
self.tf_training_env.action_spec())
avg_return, avg_return_per_step, avg_daily_percentage = self.compute_avg_return(
random_policy)
print(
'Random:\n\tAverage Return = {0}\n\tAverage Return Per Step = {1}\n\tPercent = {2}%'
.format(avg_return, avg_return_per_step, avg_daily_percentage))
self.gym_training_env.save_feature_distribution(self.name)
# Agent
avg_return, avg_return_per_step, avg_daily_percentage = self.compute_avg_return(
self.tf_agent.policy)
print(
'Agent :\n\tAverage Return = {0}\n\tAverage Return Per Step = {1}\n\tPercent = {2}%'
.format(avg_return, avg_return_per_step, avg_daily_percentage))
self.eval_env.reset()
self.eval_env.run_and_save_evaluation(str(0))
self.gym_training_env.save_feature_distribution(self.name)
evaluations = [self.get_evaluation()]
returns = [self.eval_env.returns]
actions_over_time_list = [self.eval_env.action_sets_over_time]
# Collect initial replay data.
print(
'Initializing replay buffer by collecting experience for {} steps with '
'a random policy.'.format(INIT_COLLECT_STEPS))
initial_collect_driver_random.run()
results = metric_utils.eager_compute(
eval_metrics,
self.tf_training_env,
eval_policy,
num_episodes=NUM_EVAL_EPISODES,
train_step=global_step,
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(
self.tf_training_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# 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=BATCH_SIZE, num_steps=2).unbatch().filter(
_filter_invalid_transition).batch(BATCH_SIZE).prefetch(5)
# Dataset generates trajectories with shape [Bx2x...]
iterator = iter(dataset)
def _train_step():
try:
experience, _ = next(iterator)
return self.tf_agent.train(experience)
except Exception as e:
print("Caught Exception:", e)
return 1e-20
train_step = common.function(_train_step)
for _ in range(training_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(STEP_ITERATIONS):
train_loss = train_step()
time_acc += time.time() - start_time
self.global_step_val = global_step.numpy()
if self.global_step_val % LOG_INTERVAL == 0:
steps_per_sec = (self.global_step_val -
timed_at_step) / time_acc
print(
self.name,
'\nstep = {0:d}:\n\tloss = {1:f}\n\t{2:.3f} steps/sec'.
format(self.global_step_val, train_loss.loss,
steps_per_sec))
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
timed_at_step = self.global_step_val
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2])
if self.global_step_val % EVAL_INTERVAL == 0:
results = metric_utils.eager_compute(
eval_metrics,
self.tf_training_env,
eval_policy,
num_episodes=NUM_EVAL_EPISODES,
train_step=global_step,
summary_prefix='Metrics',
)
metric_utils.log_metrics(eval_metrics)
avg_return, avg_return_per_step, avg_daily_percentage = self.compute_avg_return(
self.tf_agent.policy)
print(
self.name,
'\nstep = {0}:\n\tloss = {1}\n\tAverage Return = {2}\n\tAverage Return Per Step = {3}\n\tPercent = {4}%'
.format(self.global_step_val, train_loss.loss,
avg_return, avg_return_per_step,
avg_daily_percentage))
self.eval_env.reset()
self.eval_env.run_and_save_evaluation(
str(self.global_step_val // EVAL_INTERVAL))
self.gym_training_env.save_feature_distribution(self.name)
if avg_daily_percentage == returns[-1]:
"---- Average return did not change since last time. Breaking loop."
break
evaluations.append(self.get_evaluation())
returns.append(self.eval_env.returns)
actions_over_time_list.append(
self.eval_env.action_sets_over_time)
train_checkpointer.save(global_step=self.global_step_val)
policy_checkpointer.save(global_step=self.global_step_val)
rb_checkpointer.save(global_step=self.global_step_val)
training_report = util.load_training_report()
agent_report = training_report.get(self.name, dict())
agent_report["Training Results"] = returns
agent_report["Evaluations"] = [max(e, 0.0) for e in evaluations]
bins = [0.1 * i - 0.0000001 for i in range(11)]
agent_report["Histograms"] = [
str(list(map(int,
np.histogram(actions, bins, density=True)[0])))
for actions in actions_over_time_list
]
training_report[self.name] = agent_report
util.save_training_report(training_report)
print("---- Average-daily-percentage over training period for",
self.name)
print("\t\t", avg_daily_percentage)
self.save()
self.reset()
def train_eval(
root_dir,
experiment_name, # experiment name
env_name='carla-v0',
num_iterations=int(1e7),
model_network_ctor_type='non-hierarchical', # model net
input_names=['camera', 'lidar'], # names for inputs
reconstruct_names=['roadmap'], # names for masks
pixor_names=['vh_clas', 'vh_regr', 'pixor_state'], # names for pixor outputs
reconstruct_pixor_state=True, # whether to reconstruct pixor_state
extra_names=['state'], # extra inputs
obs_size=64, # size of observation image
pixor_size=64, # size of pixor output image
perception_weight=1.0, # weight of perception part loss
# Params for collect
initial_collect_steps=1000,
replay_buffer_capacity=int(5e4+1),
# Params for train
training=True, # whether to train, or just evaluate
model_batch_size=32, # model training batch size
sequence_length=10, # number of timesteps to train model
model_learning_rate=1e-4, # learning rate for model training
gradient_clipping=None,
# Params for eval
num_eval_episodes=10,
eval_interval=2000,
# Params for summaries and logging
num_images_per_summary=1, # images for each summary
train_checkpoint_interval=2000,
log_interval=200,
summary_interval=2000,
summaries_flush_secs=10,
summarize_grads_and_vars=False,
gpu_allow_growth=True, # GPU memory growth
gpu_memory_limit=None, # GPU memory limit
action_repeat=1): # Name of single observation channel, ['camera', 'lidar', 'birdeye']
"""A simple train and eval for SLAC."""
# Setup GPU
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)])
# Get train and eval direction
root_dir = os.path.expanduser(root_dir)
root_dir = os.path.join(root_dir, env_name, experiment_name)
# Get summary writers
summary_writer = tf.summary.create_file_writer(
root_dir, flush_millis=summaries_flush_secs * 1000)
summary_writer.set_as_default()
# Eval metrics
eval_metrics = [
tf_metrics.AverageReturnMetric(
name='AverageReturnEvalPolicy', buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(
name='AverageEpisodeLengthEvalPolicy',
buffer_size=num_eval_episodes),
]
global_step = tf.compat.v1.train.get_or_create_global_step()
# Whether to record for summary
with tf.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
# Create Carla environment
py_env, eval_py_env = load_carla_env(env_name='carla-v0', lidar_bin=32/obs_size, pixor_size=pixor_size,
obs_channels=list(set(input_names+reconstruct_names+pixor_names+extra_names)), action_repeat=action_repeat)
tf_env = tf_py_environment.TFPyEnvironment(py_env)
eval_tf_env = tf_py_environment.TFPyEnvironment(eval_py_env)
fps = int(np.round(1.0 / (py_env.dt * action_repeat)))
# Specs
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
# Get model network
if model_network_ctor_type == 'hierarchical':
model_network_ctor = sequential_latent_pixor_network.PixorSLMHierarchical
else:
raise NotImplementedError
model_net = model_network_ctor(
input_names, reconstruct_names, obs_size=obs_size, pixor_size=pixor_size,
reconstruct_pixor_state=reconstruct_pixor_state, perception_weight=perception_weight)
# Build the perception agent
actor_network = state_based_heuristic_actor_network.StateBasedHeuristicActorNetwork(
observation_spec['state'],
action_spec,
desired_speed=9
)
tf_agent = perception_agent.PerceptionAgent(
time_step_spec,
action_spec,
actor_network=actor_network,
model_network=model_net,
model_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=model_learning_rate),
num_images_per_summary=num_images_per_summary,
sequence_length=sequence_length,
gradient_clipping=gradient_clipping,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step,
fps=fps)
tf_agent.initialize()
# Train metrics
env_steps = tf_metrics.EnvironmentSteps()
average_return = tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size)
train_metrics = [
tf_metrics.NumberOfEpisodes(),
env_steps,
average_return,
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size),
]
# Get policies
eval_policy = tf_agent.policy
initial_collect_policy = tf_agent.collect_policy
# Checkpointers
train_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'train'),
agent=tf_agent,
global_step=global_step,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'),
max_to_keep=2)
train_checkpointer.initialize_or_restore()
model_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'model'),
model=model_net,
max_to_keep=2)
# Evaluation
compute_summaries(
eval_metrics,
eval_tf_env,
eval_policy,
train_step=global_step,
summary_writer=summary_writer,
num_episodes=num_eval_episodes,
num_episodes_to_render=num_images_per_summary,
model_net=model_net,
fps=10,
image_keys=['camera', 'lidar', 'roadmap'],
pixor_size=pixor_size)
# Collect/restore data and train
if training:
# Get replay buffer
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=1, # No parallel environments
max_length=replay_buffer_capacity)
replay_observer = [replay_buffer.add_batch]
# Replay buffer checkpointer
rb_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer)
rb_checkpointer.initialize_or_restore()
# Collect driver
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=initial_collect_steps)
# Optimize the performance by using tf functions
initial_collect_driver.run = common.function(initial_collect_driver.run)
# Collect initial replay data.
if (global_step.numpy() == 0 and replay_buffer.num_frames() == 0):
logging.info(
'Collecting experience for %d steps '
'with a model-based policy.', initial_collect_steps)
initial_collect_driver.run()
rb_checkpointer.save(global_step=global_step.numpy())
# Dataset generates trajectories with shape [Bxslx...]
dataset = replay_buffer.as_dataset(
num_parallel_calls=3,
sample_batch_size=model_batch_size,
num_steps=sequence_length + 1).prefetch(3)
iterator = iter(dataset)
# Get train model step
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
train_step = common.function(train_step)
# Start training
for iteration in range(num_iterations):
loss = train_step()
# Log training information
if global_step.numpy() % log_interval == 0:
logging.info('global steps = %d, model loss = %f', global_step.numpy(), loss.loss)
# Get training metrics
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step.numpy())
# Evaluation
if global_step.numpy() % eval_interval == 0:
# Log evaluation metrics
compute_summaries(
eval_metrics,
eval_tf_env,
eval_policy,
train_step=global_step,
summary_writer=summary_writer,
num_episodes=num_eval_episodes,
num_episodes_to_render=num_images_per_summary,
model_net=model_net,
fps=10,
image_keys=['camera', 'lidar', 'roadmap'],
pixor_size=pixor_size)
# Save checkpoints
global_step_val = global_step.numpy()
if global_step_val % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step_val)
model_checkpointer.save(global_step=global_step_val)
def train_eval(
root_dir,
env_load_fn=get_env,
random_seed=None,
# Params for collect
num_environment_steps=25000000,
collect_episodes_per_iteration=10,
num_parallel_environments=10,
replay_buffer_capacity=1001, # Per-environment
# Params for train
num_epochs=10,
learning_rate=1e-4,
# Params for eval
num_eval_episodes=30,
eval_interval=500,
# Params for summaries and logging
train_checkpoint_interval=500,
policy_checkpoint_interval=500,
policy_save_interval=10000,
log_interval=50,
summary_interval=50,
summaries_flush_secs=1,
use_tf_functions=True,
debug_summaries=False,
summarize_grads_and_vars=False):
if random_seed is not None:
tf.set_random_seed(random_seed)
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
saved_model_dir = os.path.join(root_dir, 'policy_saved_model')
logging.info('Running %d episodes in parallel' % num_parallel_environments)
logging.info('Collecting %d episodes per step' %
collect_episodes_per_iteration)
logging.info('Using replay buffer capacity of %d' % replay_buffer_capacity)
train_summary_writer = tf.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_tf_env = tf_py_environment.TFPyEnvironment(env_load_fn())
tf_env = tf_py_environment.TFPyEnvironment(
parallel_py_environment.ParallelPyEnvironment(
[lambda: env_load_fn()] * num_parallel_environments))
actor_net, value_net = get_actor_and_value_network(
tf_env.action_spec(), tf_env.observation_spec())
train_steps = tf.Variable(0)
with tf.summary.record_if(
lambda: tf.math.equal(train_steps % summary_interval, 0)):
tf_agent = get_agent(time_step_spec=tf_env.time_step_spec(),
action_spec=tf_env.action_spec(),
actor_net=actor_net,
value_net=value_net,
num_epochs=num_epochs,
step_counter=train_steps,
learning_rate=learning_rate)
tf_agent.initialize()
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
step_metrics, train_metrics, eval_metrics = get_metrics(
n_parallel_env=num_parallel_environments,
num_eval_episodes=num_eval_episodes)
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
tf_agent.collect_data_spec,
batch_size=num_parallel_environments,
max_length=replay_buffer_capacity)
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=tf_agent,
global_step=train_steps,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
train_dir, 'policy'),
policy=eval_policy,
global_step=train_steps)
saved_model = policy_saver.PolicySaver(eval_policy,
train_step=train_steps)
train_checkpointer.initialize_or_restore()
collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_episodes=collect_episodes_per_iteration)
def train_step():
trajectories = replay_buffer.gather_all()
return tf_agent.train(experience=trajectories)
if use_tf_functions:
# TODO(b/123828980): Enable once the cause for slowdown was identified.
collect_driver.run = common.function(collect_driver.run,
autograph=False)
tf_agent.train = common.function(tf_agent.train, autograph=False)
train_step = common.function(train_step)
collect_time = 0
train_time = 0
timed_at_step = global_step.numpy()
while environment_steps_metric.result() < num_environment_steps:
global_step_val = global_step.numpy()
if global_step_val % eval_interval == 0:
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
start_time = time.time()
collect_driver.run()
collect_time += time.time() - start_time
start_time = time.time()
total_loss, _ = train_step()
replay_buffer.clear()
train_time += time.time() - start_time
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=step_metrics)
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)
logging.info('collect_time = %.3f, train_time = %.3f',
collect_time, train_time)
with tf.compat.v2.summary.record_if(True):
tf.compat.v2.summary.scalar(name='global_steps_per_sec',
data=steps_per_sec,
step=global_step)
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 % policy_save_interval == 0:
saved_model_path = os.path.join(
saved_model_dir,
'policy_' + ('%d' % global_step_val).zfill(9))
saved_model.save(saved_model_path)
timed_at_step = global_step_val
collect_time = 0
train_time = 0
# One final eval before exiting.
metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
def train_eval(
root_dir,
experiment_name, # experiment name
env_name='carla-v0',
agent_name='sac', # agent's name
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_type='non-hierarchical', # model net
input_names=['camera', 'lidar'], # names for inputs
mask_names=['birdeye'], # names for masks
preprocessing_combiner=tf.keras.layers.Add(
), # takes a flat list of tensors and combines them
actor_lstm_size=(40, ), # lstm size for actor
critic_lstm_size=(40, ), # lstm size for critic
actor_output_fc_layers=(100, ), # lstm output
critic_output_fc_layers=(100, ), # lstm output
epsilon_greedy=0.1, # exploration parameter for DQN
q_learning_rate=1e-3, # q learning rate for DQN
ou_stddev=0.2, # exploration paprameter for DDPG
ou_damping=0.15, # exploration parameter for DDPG
dqda_clipping=None, # for DDPG
exploration_noise_std=0.1, # exploration paramter for td3
actor_update_period=2, # for td3
# Params for collect
initial_collect_steps=1000,
collect_steps_per_iteration=1,
replay_buffer_capacity=int(1e5),
# Params for target update
target_update_tau=0.005,
target_update_period=1,
# Params for train
train_steps_per_iteration=1,
initial_model_train_steps=100000, # initial model training
batch_size=256,
model_batch_size=32, # model training batch size
sequence_length=4, # number of timesteps to train model
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
model_learning_rate=1e-4, # learning rate for model training
td_errors_loss_fn=tf.losses.mean_squared_error,
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, # images for each summary
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,
gpu_allow_growth=True, # GPU memory growth
gpu_memory_limit=None, # GPU memory limit
action_repeat=1
): # Name of single observation channel, ['camera', 'lidar', 'birdeye']
# Setup GPU
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)
])
# Get train and eval directories
root_dir = os.path.expanduser(root_dir)
root_dir = os.path.join(root_dir, env_name, experiment_name)
# Get summary writers
summary_writer = tf.summary.create_file_writer(
root_dir, flush_millis=summaries_flush_secs * 1000)
summary_writer.set_as_default()
# Eval metrics
eval_metrics = [
tf_metrics.AverageReturnMetric(name='AverageReturnEvalPolicy',
buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(
name='AverageEpisodeLengthEvalPolicy',
buffer_size=num_eval_episodes),
]
global_step = tf.compat.v1.train.get_or_create_global_step()
# Whether to record for summary
with tf.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
# Create Carla environment
if agent_name == 'latent_sac':
py_env, eval_py_env = load_carla_env(env_name='carla-v0',
obs_channels=input_names +
mask_names,
action_repeat=action_repeat)
elif agent_name == 'dqn':
py_env, eval_py_env = load_carla_env(env_name='carla-v0',
discrete=True,
obs_channels=input_names,
action_repeat=action_repeat)
else:
py_env, eval_py_env = load_carla_env(env_name='carla-v0',
obs_channels=input_names,
action_repeat=action_repeat)
tf_env = tf_py_environment.TFPyEnvironment(py_env)
eval_tf_env = tf_py_environment.TFPyEnvironment(eval_py_env)
fps = int(np.round(1.0 / (py_env.dt * action_repeat)))
# Specs
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
## Make tf agent
if agent_name == 'latent_sac':
# Get model network for latent sac
if model_network_ctor_type == 'hierarchical':
model_network_ctor = sequential_latent_network.SequentialLatentModelHierarchical
elif model_network_ctor_type == 'non-hierarchical':
model_network_ctor = sequential_latent_network.SequentialLatentModelNonHierarchical
else:
raise NotImplementedError
model_net = model_network_ctor(input_names,
input_names + mask_names)
# Get the latent spec
latent_size = model_net.latent_size
latent_observation_spec = tensor_spec.TensorSpec((latent_size, ),
dtype=tf.float32)
latent_time_step_spec = ts.time_step_spec(
observation_spec=latent_observation_spec)
# Get actor and critic net
actor_net = actor_distribution_network.ActorDistributionNetwork(
latent_observation_spec,
action_spec,
fc_layer_params=actor_fc_layers,
continuous_projection_net=normal_projection_net)
critic_net = critic_network.CriticNetwork(
(latent_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)
# Build the inner SAC agent based on latent space
inner_agent = sac_agent.SacAgent(
latent_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)
inner_agent.initialize()
# Build the latent sac agent
tf_agent = latent_sac_agent.LatentSACAgent(
time_step_spec,
action_spec,
inner_agent=inner_agent,
model_network=model_net,
model_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=model_learning_rate),
model_batch_size=model_batch_size,
num_images_per_summary=num_images_per_summary,
sequence_length=sequence_length,
gradient_clipping=gradient_clipping,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step,
fps=fps)
else:
# Set up preprosessing layers for dictionary observation inputs
preprocessing_layers = collections.OrderedDict()
for name in input_names:
preprocessing_layers[name] = Preprocessing_Layer(32, 256)
if len(input_names) < 2:
preprocessing_combiner = None
if agent_name == 'dqn':
q_rnn_net = q_rnn_network.QRnnNetwork(
observation_spec,
action_spec,
preprocessing_layers=preprocessing_layers,
preprocessing_combiner=preprocessing_combiner,
input_fc_layer_params=critic_joint_fc_layers,
lstm_size=critic_lstm_size,
output_fc_layer_params=critic_output_fc_layers)
tf_agent = dqn_agent.DqnAgent(
time_step_spec,
action_spec,
q_network=q_rnn_net,
epsilon_greedy=epsilon_greedy,
n_step_update=1,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=q_learning_rate),
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)
elif agent_name == 'ddpg' or agent_name == 'td3':
actor_rnn_net = multi_inputs_actor_rnn_network.MultiInputsActorRnnNetwork(
observation_spec,
action_spec,
preprocessing_layers=preprocessing_layers,
preprocessing_combiner=preprocessing_combiner,
input_fc_layer_params=actor_fc_layers,
lstm_size=actor_lstm_size,
output_fc_layer_params=actor_output_fc_layers)
critic_rnn_net = multi_inputs_critic_rnn_network.MultiInputsCriticRnnNetwork(
(observation_spec, action_spec),
preprocessing_layers=preprocessing_layers,
preprocessing_combiner=preprocessing_combiner,
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)
if agent_name == 'ddpg':
tf_agent = ddpg_agent.DdpgAgent(
time_step_spec,
action_spec,
actor_network=actor_rnn_net,
critic_network=critic_rnn_net,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_learning_rate),
ou_stddev=ou_stddev,
ou_damping=ou_damping,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
dqda_clipping=dqda_clipping,
td_errors_loss_fn=None,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars)
elif agent_name == 'td3':
tf_agent = td3_agent.Td3Agent(
time_step_spec,
action_spec,
actor_network=actor_rnn_net,
critic_network=critic_rnn_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=None,
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)
elif agent_name == 'sac':
actor_distribution_rnn_net = actor_distribution_rnn_network.ActorDistributionRnnNetwork(
observation_spec,
action_spec,
preprocessing_layers=preprocessing_layers,
preprocessing_combiner=preprocessing_combiner,
input_fc_layer_params=actor_fc_layers,
lstm_size=actor_lstm_size,
output_fc_layer_params=actor_output_fc_layers,
continuous_projection_net=normal_projection_net)
critic_rnn_net = multi_inputs_critic_rnn_network.MultiInputsCriticRnnNetwork(
(observation_spec, action_spec),
preprocessing_layers=preprocessing_layers,
preprocessing_combiner=preprocessing_combiner,
action_fc_layer_params=critic_action_fc_layers,
joint_fc_layer_params=critic_joint_fc_layers,
lstm_size=critic_lstm_size,
output_fc_layer_params=critic_output_fc_layers)
tf_agent = sac_agent.SacAgent(
time_step_spec,
action_spec,
actor_network=actor_distribution_rnn_net,
critic_network=critic_rnn_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=tf.math.
squared_difference, # make critic loss dimension compatible
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)
else:
raise NotImplementedError
tf_agent.initialize()
# Get replay buffer
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=1, # No parallel environments
max_length=replay_buffer_capacity)
replay_observer = [replay_buffer.add_batch]
# Train metrics
env_steps = tf_metrics.EnvironmentSteps()
average_return = tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size)
train_metrics = [
tf_metrics.NumberOfEpisodes(),
env_steps,
average_return,
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes, batch_size=tf_env.batch_size),
]
# Get policies
# eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
eval_policy = tf_agent.policy
initial_collect_policy = random_tf_policy.RandomTFPolicy(
time_step_spec, action_spec)
collect_policy = tf_agent.collect_policy
# Checkpointers
train_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'train'),
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(
root_dir, 'policy'),
policy=eval_policy,
global_step=global_step,
max_to_keep=2)
rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
# Collect driver
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=initial_collect_steps)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=collect_steps_per_iteration)
# Optimize the performance by using tf functions
initial_collect_driver.run = common.function(
initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
# Collect initial replay data.
if (env_steps.result() == 0 or replay_buffer.num_frames() == 0):
logging.info(
'Initializing replay buffer by collecting experience for %d steps'
'with a random policy.', initial_collect_steps)
initial_collect_driver.run()
if agent_name == 'latent_sac':
compute_summaries(eval_metrics,
eval_tf_env,
eval_policy,
train_step=global_step,
summary_writer=summary_writer,
num_episodes=1,
num_episodes_to_render=1,
model_net=model_net,
fps=10,
image_keys=input_names + mask_names)
else:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=1,
train_step=env_steps.result(),
summary_writer=summary_writer,
summary_prefix='Eval',
)
metric_utils.log_metrics(eval_metrics)
# Dataset generates trajectories with shape [Bxslx...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=sequence_length +
1).prefetch(3)
iterator = iter(dataset)
# Get train step
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
train_step = common.function(train_step)
if agent_name == 'latent_sac':
def train_model_step():
experience, _ = next(iterator)
return tf_agent.train_model(experience)
train_model_step = common.function(train_model_step)
# Training initializations
time_step = None
time_acc = 0
env_steps_before = env_steps.result().numpy()
# Start training
for iteration in range(num_iterations):
start_time = time.time()
if agent_name == 'latent_sac' and iteration < initial_model_train_steps:
train_model_step()
else:
# Run collect
time_step, _ = collect_driver.run(time_step=time_step)
# Train an iteration
for _ in range(train_steps_per_iteration):
train_step()
time_acc += time.time() - start_time
# Log training information
if global_step.numpy() % log_interval == 0:
logging.info('env steps = %d, average return = %f',
env_steps.result(), average_return.result())
env_steps_per_sec = (env_steps.result().numpy() -
env_steps_before) / time_acc
logging.info('%.3f env steps/sec', env_steps_per_sec)
tf.summary.scalar(name='env_steps_per_sec',
data=env_steps_per_sec,
step=env_steps.result())
time_acc = 0
env_steps_before = env_steps.result().numpy()
# Get training metrics
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=env_steps.result())
# Evaluation
if global_step.numpy() % eval_interval == 0:
# Log evaluation metrics
if agent_name == 'latent_sac':
compute_summaries(
eval_metrics,
eval_tf_env,
eval_policy,
train_step=global_step,
summary_writer=summary_writer,
num_episodes=num_eval_episodes,
num_episodes_to_render=num_images_per_summary,
model_net=model_net,
fps=10,
image_keys=input_names + mask_names)
else:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=env_steps.result(),
summary_writer=summary_writer,
summary_prefix='Eval',
)
metric_utils.log_metrics(eval_metrics)
# Save checkpoints
global_step_val = global_step.numpy()
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,
env,
global_step,
root_dir,
step_metrics,
name='Agent',
is_environment=False,
use_tf_functions=True,
max_steps=250,
replace_reward=True,
non_negative_regret=False,
id_num=0,
block_budget_weight=0.,
# Architecture hparams
use_rnn=True,
learning_rate=1e-4,
actor_fc_layers=(32, 32),
value_fc_layers=(32, 32),
lstm_size=(128, ),
conv_filters=8,
conv_kernel=3,
scalar_fc=5,
entropy_regularization=0.,
xy_dim=None,
# Training & logging settings
num_epochs=25,
num_eval_episodes=5,
num_parallel_envs=5,
replay_buffer_capacity=1001,
debug_summaries=True,
summarize_grads_and_vars=True,
):
"""Initializes agent, replay buffer, metrics, and checkpointing.
Args:
env: An AdversarialTfPyEnvironment with specs and advesary specs.
global_step: A tf variable tracking the global step.
root_dir: Path to directory where metrics and checkpoints should be saved.
step_metrics: A list of tf-agents metrics which represent the x-axis
during training, such as the number of episodes or the number of
environment steps.
name: The name of this agent, e.g. 'Adversary'.
is_environment: If True, will use the adversary specs from the environment
and construct a network with additional inputs for the adversary.
use_tf_functions: If True, will use tf.function to wrap the agent's train
function.
max_steps: The maximum number of steps the agent is allowed to interact
with the environment in every data collection loop.
replace_reward: If False, will not modify the reward stored in the agent's
trajectories. This means the agent will be trained with the default
environment reward rather than regret.
non_negative_regret: If True, will ensure that the regret reward cannot
be below 0.
id_num: The ID number of this agent within the population of agents of the
same type. I.e. this is adversary agent 3.
block_budget_weight: Weight to place on the adversary's block budget
reward. Default is 0 for no block budget.
use_rnn: If True, will use an RNN within the network architecture.
learning_rate: The learning rate used to initialize the optimizer for this
agent.
actor_fc_layers: The number and size of fully connected layers in the
policy.
value_fc_layers: The number and size of fully connected layers in the
critic / value network.
lstm_size: The number of LSTM cells in the RNN.
conv_filters: The number of convolution filters.
conv_kernel: The width of the convolution kernel.
scalar_fc: The width of the fully-connected layer which inputs a scalar.
entropy_regularization: Entropy regularization coefficient.
xy_dim: Certain adversaries take in the current (x,y) position as a
one-hot vector. In this case, the maximum value for x or y is required
to create the one-hot representation.
num_epochs: Number of epochs for computing PPO policy updates.
num_eval_episodes: Number of evaluation episodes be eval step, used as
batch size to initialize eval metrics.
num_parallel_envs: Number of parallel environments used in trainin, used
as batch size for training metrics and rewards.
replay_buffer_capacity: Capacity of this agent's replay buffer.
debug_summaries: Log additional summaries from the PPO agent.
summarize_grads_and_vars: If True, logs gradient norms and variances in
PPO agent.
"""
self.name = name
self.id = id_num
self.max_steps = max_steps
self.is_environment = is_environment
self.replace_reward = replace_reward
self.non_negative_regret = non_negative_regret
self.block_budget_weight = block_budget_weight
with tf.name_scope(self.name):
self.optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate)
logging.info('\tCalculating specs and building networks...')
if is_environment:
self.time_step_spec = env.adversary_time_step_spec
self.action_spec = env.adversary_action_spec
self.observation_spec = env.adversary_observation_spec
(self.actor_net, self.value_net
) = multigrid_networks.construct_multigrid_networks(
self.observation_spec,
self.action_spec,
use_rnns=use_rnn,
actor_fc_layers=actor_fc_layers,
value_fc_layers=value_fc_layers,
lstm_size=lstm_size,
conv_filters=conv_filters,
conv_kernel=conv_kernel,
scalar_fc=scalar_fc,
scalar_name='time_step',
scalar_dim=self.observation_spec['time_step'].maximum + 1,
random_z=True,
xy_dim=xy_dim)
else:
self.time_step_spec = env.time_step_spec()
self.action_spec = env.action_spec()
self.observation_spec = env.observation_spec()
(self.actor_net, self.value_net
) = multigrid_networks.construct_multigrid_networks(
self.observation_spec,
self.action_spec,
use_rnns=use_rnn,
actor_fc_layers=actor_fc_layers,
value_fc_layers=value_fc_layers,
lstm_size=lstm_size,
conv_filters=conv_filters,
conv_kernel=conv_kernel,
scalar_fc=scalar_fc)
self.tf_agent = ppo_clip_agent.PPOClipAgent(
self.time_step_spec,
self.action_spec,
self.optimizer,
actor_net=self.actor_net,
value_net=self.value_net,
entropy_regularization=entropy_regularization,
importance_ratio_clipping=0.2,
normalize_observations=False,
normalize_rewards=False,
use_gae=True,
num_epochs=num_epochs,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=global_step)
self.tf_agent.initialize()
self.eval_policy = self.tf_agent.policy
self.collect_policy = self.tf_agent.collect_policy
logging.info('\tAllocating replay buffer ...')
self.replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
self.tf_agent.collect_data_spec,
batch_size=num_parallel_envs,
max_length=replay_buffer_capacity)
logging.info('\t\tRB capacity: %i', self.replay_buffer.capacity)
self.final_reward = tf.zeros(shape=(num_parallel_envs),
dtype=tf.float32)
self.enemy_max = tf.zeros(shape=(num_parallel_envs),
dtype=tf.float32)
# Creates train metrics
self.step_metrics = step_metrics
self.train_metrics = step_metrics + [
tf_metrics.AverageEpisodeLengthMetric(
batch_size=num_parallel_envs,
name=name + '_AverageEpisodeLength')
]
self.eval_metrics = [
tf_metrics.AverageEpisodeLengthMetric(
batch_size=num_eval_episodes,
name=name + '_AverageEpisodeLength')
]
if is_environment:
self.env_train_metric = adversarial_eval.AdversarialEnvironmentScalar(
batch_size=num_parallel_envs,
name=name + '_AdversaryReward')
self.env_eval_metric = adversarial_eval.AdversarialEnvironmentScalar(
batch_size=num_eval_episodes,
name=name + '_AdversaryReward')
else:
self.train_metrics.append(
tf_metrics.AverageReturnMetric(
batch_size=num_parallel_envs,
name=name + '_AverageReturn'))
self.eval_metrics.append(
tf_metrics.AverageReturnMetric(
batch_size=num_eval_episodes,
name=name + '_AverageReturn'))
self.metrics_group = metric_utils.MetricsGroup(
self.train_metrics, name + '_train_metrics')
self.observers = self.train_metrics + [
self.replay_buffer.add_batch
]
self.train_dir = os.path.join(root_dir, 'train', name, str(id_num))
self.eval_dir = os.path.join(root_dir, 'eval', name, str(id_num))
self.train_checkpointer = common.Checkpointer(
ckpt_dir=self.train_dir,
agent=self.tf_agent,
global_step=global_step,
metrics=self.metrics_group,
)
self.policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(self.train_dir, 'policy'),
policy=self.eval_policy,
global_step=global_step)
self.saved_model = policy_saver.PolicySaver(self.eval_policy,
train_step=global_step)
self.saved_model_dir = os.path.join(root_dir, 'policy_saved_model',
name, str(id_num))
self.train_checkpointer.initialize_or_restore()
if use_tf_functions:
self.tf_agent.train = common.function(self.tf_agent.train,
autograph=False)
self.total_loss = None
self.extra_loss = None
self.loss_divergence_counter = 0
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 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,
use_tf_functions=True,
# 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 = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name))
eval_tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name))
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)
tf_agent.initialize()
# 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]
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_py_metric.TFPyMetric(py_metrics.AverageReturnMetric()),
tf_py_metric.TFPyMetric(py_metrics.AverageEpisodeLengthMetric()),
]
eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
collect_policy = tf_agent.collect_policy
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=eval_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)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer,
num_steps=initial_collect_steps)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=collect_steps_per_iteration)
if use_tf_functions:
initial_collect_driver.run = common.function(initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
# Collect initial replay data.
logging.info(
'Initializing replay buffer by collecting experience for %d steps with '
'a random policy.', initial_collect_steps)
initial_collect_driver.run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(
num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=2).prefetch(3)
iterator = iter(dataset)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
experience, _ = next(iterator)
train_loss = tf_agent.train(experience)
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('step = %d, loss = %f', global_step.numpy(),
train_loss.loss)
steps_per_sec = (global_step.numpy() - timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
tf.compat.v2.summary.scalar(
name='global_steps_per_sec', data=steps_per_sec, step=global_step)
timed_at_step = global_step.numpy()
time_acc = 0
for train_metric in train_metrics:
train_metric.tf_summaries(
train_step=global_step, step_metrics=train_metrics[:2])
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix='Metrics',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, global_step.numpy())
metric_utils.log_metrics(eval_metrics)
global_step_val = global_step.numpy()
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)
return train_loss
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
eval_env_name=None,
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.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 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),
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
if 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_env_name = eval_env_name or env_name
eval_py_env = env_load_fn(eval_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.compat.v1.train.AdamOptimizer(
learning_rate=actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_learning_rate),
ou_stddev=ou_stddev,
ou_damping=ou_damping,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
dqda_clipping=dqda_clipping,
td_errors_loss_fn=td_errors_loss_fn,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
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 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)
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,
)
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 = 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, _ = 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 play(root_dir,
env,
algorithm,
checkpoint_name=None,
greedy_predict=True,
random_seed=None,
num_episodes=10,
sleep_time_per_step=0.01,
record_file=None,
use_tf_functions=True):
"""Play using the latest checkpoint under `train_dir`.
The following example record the play of a trained model to a mp4 video:
```bash
python -m alf.bin.play \
--root_dir=~/tmp/bullet_humanoid/ppo2/ppo2-11 \
--num_episodes=1 \
--record_file=ppo_bullet_humanoid.mp4
```
Args:
root_dir (str): same as the root_dir used for `train()`
env (TFEnvironment): the environment
algorithm (OnPolicyAlgorithm): the training algorithm
checkpoint_name (str): name of the checkpoint (e.g. 'ckpt-12800`).
If None, the latest checkpoint under train_dir will be used.
greedy_predict (bool): use greedy action for evaluation.
random_seed (None|int): random seed, a random seed is used if None
num_episodes (int): number of episodes to play
sleep_time_per_step (float): sleep so many seconds for each step
record_file (str): if provided, video will be recorded to a file
instead of shown on the screen.
use_tf_functions (bool): whether to use tf.function
"""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
if random_seed is not None:
random.seed(random_seed)
np.random.seed(random_seed)
tf.random.set_seed(random_seed)
global_step = get_global_counter()
driver = OnPolicyDriver(env=env,
algorithm=algorithm,
training=False,
greedy_predict=greedy_predict)
ckpt_dir = os.path.join(train_dir, 'algorithm')
checkpoint = tf.train.Checkpoint(algorithm=algorithm,
metrics=metric_utils.MetricsGroup(
driver.get_metrics(), 'metrics'),
global_step=global_step)
if checkpoint_name is not None:
ckpt_path = os.path.join(ckpt_dir, checkpoint_name)
else:
ckpt_path = tf.train.latest_checkpoint(ckpt_dir)
if ckpt_path is not None:
logging.info("Restore from checkpoint %s" % ckpt_path)
checkpoint.restore(ckpt_path)
else:
logging.info("Checkpoint is not found at %s" % ckpt_dir)
if not use_tf_functions:
tf.config.experimental_run_functions_eagerly(True)
recorder = None
if record_file is not None:
recorder = VideoRecorder(env.pyenv.envs[0], path=record_file)
else:
# pybullet_envs need to render() before reset() to enable mode='human'
env.pyenv.envs[0].render(mode='human')
env.reset()
if recorder:
recorder.capture_frame()
time_step = driver.get_initial_time_step()
policy_state = driver.get_initial_policy_state()
episode_reward = 0.
episode_length = 0
episodes = 0
while episodes < num_episodes:
time_step, policy_state = driver.run(max_num_steps=1,
time_step=time_step,
policy_state=policy_state)
if recorder:
recorder.capture_frame()
else:
env.pyenv.envs[0].render(mode='human')
time.sleep(sleep_time_per_step)
episode_reward += float(time_step.reward)
if time_step.is_last():
logging.info("episode_length=%s episode_reward=%s" %
(episode_length, episode_reward))
episode_reward = 0.
episode_length = 0.
episodes += 1
else:
episode_length += 1
if recorder:
recorder.close()
env.reset()
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(
batch_size=num_parallel_environments),
tf_metrics.AverageEpisodeLengthMetric(
batch_size=num_parallel_environments),
]
# 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)
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()
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.compat.v2.summary.scalar(
name='global_steps_per_sec',
data=steps_per_second_ph,
step=global_step)
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, summary_ops])
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(
##############################################
# 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,
environment_name="broken_reacher",
num_iterations=1000000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
initial_collect_steps=10000,
real_initial_collect_steps=10000,
collect_steps_per_iteration=1,
real_collect_interval=10,
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,
classifier_learning_rate=3e-4,
alpha_learning_rate=3e-4,
td_errors_loss_fn=tf.math.squared_difference,
gamma=0.99,
reward_scale_factor=0.1,
gradient_clipping=None,
use_tf_functions=True,
# 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=True,
summarize_grads_and_vars=False,
train_on_real=False,
delta_r_warmup=0,
random_seed=0,
checkpoint_dir=None,
):
"""A simple train and eval for SAC."""
np.random.seed(random_seed)
tf.random.set_seed(random_seed)
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)
if environment_name == "broken_reacher":
get_env_fn = darc_envs.get_broken_reacher_env
elif environment_name == "half_cheetah_obstacle":
get_env_fn = darc_envs.get_half_cheetah_direction_env
elif environment_name == "inverted_pendulum":
get_env_fn = darc_envs.get_inverted_pendulum_env
elif environment_name.startswith("broken_joint"):
base_name = environment_name.split("broken_joint_")[1]
get_env_fn = functools.partial(darc_envs.get_broken_joint_env,
env_name=base_name)
elif environment_name.startswith("falling"):
base_name = environment_name.split("falling_")[1]
get_env_fn = functools.partial(darc_envs.get_falling_env,
env_name=base_name)
else:
raise NotImplementedError("Unknown environment: %s" % environment_name)
eval_name_list = ["sim", "real"]
eval_env_list = [get_env_fn(mode) for mode in eval_name_list]
eval_metrics_list = []
for name in eval_name_list:
eval_metrics_list.append([
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes,
name="AverageReturn_%s" % name),
])
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_real = get_env_fn("real")
if train_on_real:
tf_env = get_env_fn("real")
else:
tf_env = get_env_fn("sim")
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=(
tanh_normal_projection_network.TanhNormalProjectionNetwork),
)
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,
kernel_initializer="glorot_uniform",
last_kernel_initializer="glorot_uniform",
)
classifier = classifiers.build_classifier(observation_spec,
action_spec)
tf_agent = darc_agent.DarcAgent(
time_step_spec,
action_spec,
actor_network=actor_net,
critic_network=critic_net,
classifier=classifier,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_learning_rate),
classifier_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=classifier_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,
)
tf_agent.initialize()
# 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]
real_replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=1,
max_length=replay_buffer_capacity,
)
real_replay_observer = [real_replay_buffer.add_batch]
sim_train_metrics = [
tf_metrics.NumberOfEpisodes(name="NumberOfEpisodesSim"),
tf_metrics.EnvironmentSteps(name="EnvironmentStepsSim"),
tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
name="AverageReturnSim",
),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
name="AverageEpisodeLengthSim",
),
]
real_train_metrics = [
tf_metrics.NumberOfEpisodes(name="NumberOfEpisodesReal"),
tf_metrics.EnvironmentSteps(name="EnvironmentStepsReal"),
tf_metrics.AverageReturnMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
name="AverageReturnReal",
),
tf_metrics.AverageEpisodeLengthMetric(
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size,
name="AverageEpisodeLengthReal",
),
]
eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
collect_policy = tf_agent.collect_policy
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=tf_agent,
global_step=global_step,
metrics=metric_utils.MetricsGroup(
sim_train_metrics + real_train_metrics, "train_metrics"),
)
policy_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(train_dir, "policy"),
policy=eval_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, real_replay_buffer),
)
if checkpoint_dir is not None:
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
assert checkpoint_path is not None
train_checkpointer._load_status = train_checkpointer._checkpoint.restore( # pylint: disable=protected-access
checkpoint_path)
train_checkpointer._load_status.initialize_or_restore() # pylint: disable=protected-access
else:
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
if replay_buffer.num_frames() == 0:
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + sim_train_metrics,
num_steps=initial_collect_steps,
)
real_initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env_real,
initial_collect_policy,
observers=real_replay_observer + real_train_metrics,
num_steps=real_initial_collect_steps,
)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + sim_train_metrics,
num_steps=collect_steps_per_iteration,
)
real_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env_real,
collect_policy,
observers=real_replay_observer + real_train_metrics,
num_steps=collect_steps_per_iteration,
)
config_str = gin.operative_config_str()
logging.info(config_str)
with tf.compat.v1.gfile.Open(os.path.join(root_dir, "operative.gin"),
"w") as f:
f.write(config_str)
if use_tf_functions:
initial_collect_driver.run = common.function(
initial_collect_driver.run)
real_initial_collect_driver.run = common.function(
real_initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
real_collect_driver.run = common.function(real_collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
# Collect initial replay data.
if replay_buffer.num_frames() == 0:
logging.info(
"Initializing replay buffer by collecting experience for %d steps with "
"a random policy.",
initial_collect_steps,
)
initial_collect_driver.run()
real_initial_collect_driver.run()
for eval_name, eval_env, eval_metrics in zip(eval_name_list,
eval_env_list,
eval_metrics_list):
metric_utils.eager_compute(
eval_metrics,
eval_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix="Metrics-%s" % eval_name,
)
metric_utils.log_metrics(eval_metrics)
time_step = None
real_time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
timed_at_step = global_step.numpy()
time_acc = 0
# 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=batch_size, num_steps=2).unbatch().filter(
_filter_invalid_transition).batch(batch_size).prefetch(5))
real_dataset = (real_replay_buffer.as_dataset(
sample_batch_size=batch_size, num_steps=2).unbatch().filter(
_filter_invalid_transition).batch(batch_size).prefetch(5))
# Dataset generates trajectories with shape [Bx2x...]
iterator = iter(dataset)
real_iterator = iter(real_dataset)
def train_step():
experience, _ = next(iterator)
real_experience, _ = next(real_iterator)
return tf_agent.train(experience, real_experience=real_experience)
if use_tf_functions:
train_step = common.function(train_step)
for _ in range(num_iterations):
start_time = time.time()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
assert not policy_state # We expect policy_state == ().
if (global_step.numpy() % real_collect_interval == 0
and global_step.numpy() >= delta_r_warmup):
real_time_step, policy_state = real_collect_driver.run(
time_step=real_time_step,
policy_state=policy_state,
)
for _ in range(train_steps_per_iteration):
train_loss = train_step()
time_acc += time.time() - start_time
global_step_val = global_step.numpy()
if global_step_val % log_interval == 0:
logging.info("step = %d, loss = %f", global_step_val,
train_loss.loss)
steps_per_sec = (global_step_val - timed_at_step) / time_acc
logging.info("%.3f steps/sec", steps_per_sec)
tf.compat.v2.summary.scalar(name="global_steps_per_sec",
data=steps_per_sec,
step=global_step)
timed_at_step = global_step_val
time_acc = 0
for train_metric in sim_train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=sim_train_metrics[:2])
for train_metric in real_train_metrics:
train_metric.tf_summaries(train_step=global_step,
step_metrics=real_train_metrics[:2])
if global_step_val % eval_interval == 0:
for eval_name, eval_env, eval_metrics in zip(
eval_name_list, eval_env_list, eval_metrics_list):
metric_utils.eager_compute(
eval_metrics,
eval_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=global_step,
summary_writer=eval_summary_writer,
summary_prefix="Metrics-%s" % eval_name,
)
metric_utils.log_metrics(eval_metrics)
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)
return train_loss
def train_eval(
root_dir,
env_name='cartpole',
task_name='balance',
observations_allowlist='position',
eval_env_name=None,
num_iterations=1000000,
# Params for networks.
actor_fc_layers=(400, 300),
actor_output_fc_layers=(100, ),
actor_lstm_size=(40, ),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(300, ),
critic_output_fc_layers=(100, ),
critic_lstm_size=(40, ),
num_parallel_environments=1,
# Params for collect
initial_collect_episodes=1,
collect_episodes_per_iteration=1,
replay_buffer_capacity=1000000,
# Params for target update
target_update_tau=0.05,
target_update_period=5,
# Params for train
train_steps_per_iteration=1,
batch_size=256,
critic_learning_rate=3e-4,
train_sequence_length=20,
actor_learning_rate=3e-4,
alpha_learning_rate=3e-4,
td_errors_loss_fn=tf.math.squared_difference,
gamma=0.99,
reward_scale_factor=0.1,
gradient_clipping=None,
use_tf_functions=True,
# 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 RNN SAC on DM control."""
root_dir = os.path.expanduser(root_dir)
summary_writer = tf.compat.v2.summary.create_file_writer(
root_dir, flush_millis=summaries_flush_secs * 1000)
summary_writer.set_as_default()
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
global_step = tf.compat.v1.train.get_or_create_global_step()
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
if observations_allowlist is not None:
env_wrappers = [
functools.partial(
wrappers.FlattenObservationsWrapper,
observations_allowlist=[observations_allowlist])
]
else:
env_wrappers = []
env_load_fn = functools.partial(suite_dm_control.load,
task_name=task_name,
env_wrappers=env_wrappers)
if num_parallel_environments == 1:
py_env = env_load_fn(env_name)
else:
py_env = parallel_py_environment.ParallelPyEnvironment(
[lambda: env_load_fn(env_name)] * num_parallel_environments)
tf_env = tf_py_environment.TFPyEnvironment(py_env)
eval_env_name = eval_env_name or env_name
eval_tf_env = tf_py_environment.TFPyEnvironment(
env_load_fn(eval_env_name))
time_step_spec = tf_env.time_step_spec()
observation_spec = time_step_spec.observation
action_spec = tf_env.action_spec()
actor_net = actor_distribution_rnn_network.ActorDistributionRnnNetwork(
observation_spec,
action_spec,
input_fc_layer_params=actor_fc_layers,
lstm_size=actor_lstm_size,
output_fc_layer_params=actor_output_fc_layers,
continuous_projection_net=tanh_normal_projection_network.
TanhNormalProjectionNetwork)
critic_net = critic_rnn_network.CriticRnnNetwork(
(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,
lstm_size=critic_lstm_size,
output_fc_layer_params=critic_output_fc_layers,
kernel_initializer='glorot_uniform',
last_kernel_initializer='glorot_uniform')
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)
tf_agent.initialize()
# Make the replay buffer.
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
replay_observer = [replay_buffer.add_batch]
env_steps = tf_metrics.EnvironmentSteps(prefix='Train')
average_return = tf_metrics.AverageReturnMetric(
prefix='Train',
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size)
train_metrics = [
tf_metrics.NumberOfEpisodes(prefix='Train'),
env_steps,
average_return,
tf_metrics.AverageEpisodeLengthMetric(
prefix='Train',
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size),
]
eval_policy = greedy_policy.GreedyPolicy(tf_agent.policy)
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), tf_env.action_spec())
collect_policy = tf_agent.collect_policy
train_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'train'),
agent=tf_agent,
global_step=global_step,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'policy'),
policy=eval_policy,
global_step=global_step)
rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer)
train_checkpointer.initialize_or_restore()
rb_checkpointer.initialize_or_restore()
initial_collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_episodes=initial_collect_episodes)
collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_episodes=collect_episodes_per_iteration)
if use_tf_functions:
initial_collect_driver.run = common.function(
initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
tf_agent.train = common.function(tf_agent.train)
# Collect initial replay data.
if env_steps.result() == 0 or replay_buffer.num_frames() == 0:
logging.info(
'Initializing replay buffer by collecting experience for %d episodes '
'with a random policy.', initial_collect_episodes)
initial_collect_driver.run()
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=env_steps.result(),
summary_writer=summary_writer,
summary_prefix='Eval',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, env_steps.result())
metric_utils.log_metrics(eval_metrics)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
time_acc = 0
env_steps_before = env_steps.result().numpy()
# Prepare replay buffer as dataset with invalid transitions filtered.
def _filter_invalid_transition(trajectories, unused_arg1):
# Reduce filter_fn over full trajectory sampled. The sequence is kept only
# if all elements except for the last one pass the filter. This is to
# allow training on terminal steps.
return tf.reduce_all(~trajectories.is_boundary()[:-1])
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=train_sequence_length + 1).unbatch().filter(
_filter_invalid_transition).batch(batch_size).prefetch(5)
# Dataset generates trajectories with shape [Bx2x...]
iterator = iter(dataset)
def train_step():
experience, _ = next(iterator)
return tf_agent.train(experience)
if use_tf_functions:
train_step = common.function(train_step)
for _ in range(num_iterations):
start_time = time.time()
start_env_steps = env_steps.result()
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
episode_steps = env_steps.result() - start_env_steps
# TODO(b/152648849)
for _ in range(episode_steps):
for _ in range(train_steps_per_iteration):
train_step()
time_acc += time.time() - start_time
if global_step.numpy() % log_interval == 0:
logging.info('env steps = %d, average return = %f',
env_steps.result(), average_return.result())
env_steps_per_sec = (env_steps.result().numpy() -
env_steps_before) / time_acc
logging.info('%.3f env steps/sec', env_steps_per_sec)
tf.compat.v2.summary.scalar(name='env_steps_per_sec',
data=env_steps_per_sec,
step=env_steps.result())
time_acc = 0
env_steps_before = env_steps.result().numpy()
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=env_steps.result())
if global_step.numpy() % eval_interval == 0:
results = metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
num_episodes=num_eval_episodes,
train_step=env_steps.result(),
summary_writer=summary_writer,
summary_prefix='Eval',
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, env_steps.numpy())
metric_utils.log_metrics(eval_metrics)
global_step_val = global_step.numpy()
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 train_eval(
root_dir,
env_name='cartpole',
task_name='balance',
observations_whitelist='position',
num_iterations=100000,
actor_fc_layers=(400, 300),
actor_output_fc_layers=(100, ),
actor_lstm_size=(40, ),
critic_obs_fc_layers=(400, ),
critic_action_fc_layers=None,
critic_joint_fc_layers=(300, ),
critic_output_fc_layers=(100, ),
critic_lstm_size=(40, ),
# Params for collect
initial_collect_steps=1,
collect_episodes_per_iteration=1,
replay_buffer_capacity=100000,
exploration_noise_std=0.1,
# Params for target update
target_update_tau=0.05,
target_update_period=5,
# Params for train
train_steps_per_iteration=200,
batch_size=64,
actor_update_period=2,
train_sequence_length=10,
actor_learning_rate=1e-4,
critic_learning_rate=1e-3,
dqda_clipping=None,
gamma=0.995,
reward_scale_factor=1.0,
# Params for eval
num_eval_episodes=10,
eval_interval=1000,
# Params for checkpoints, summaries, and logging
train_checkpoint_interval=10000,
policy_checkpoint_interval=5000,
rb_checkpoint_interval=10000,
log_interval=1000,
summary_interval=1000,
summaries_flush_secs=10,
debug_summaries=False,
eval_metrics_callback=None):
"""A simple train and eval for DDPG."""
root_dir = os.path.expanduser(root_dir)
train_dir = os.path.join(root_dir, 'train')
eval_dir = os.path.join(root_dir, 'eval')
train_summary_writer = tf.compat.v2.summary.create_file_writer(
train_dir, flush_millis=summaries_flush_secs * 1000)
train_summary_writer.set_as_default()
eval_summary_writer = tf.compat.v2.summary.create_file_writer(
eval_dir, flush_millis=summaries_flush_secs * 1000)
eval_metrics = [
py_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
py_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),
]
with tf.compat.v2.summary.record_if(
lambda: tf.math.equal(global_step % summary_interval, 0)):
if observations_whitelist is not None:
env_wrappers = [
functools.partial(
wrappers.FlattenObservationsWrapper,
observations_whitelist=[observations_whitelist])
]
else:
env_wrappers = []
environment = suite_dm_control.load(env_name,
task_name,
env_wrappers=env_wrappers)
tf_env = tf_py_environment.TFPyEnvironment(environment)
eval_py_env = suite_dm_control.load(env_name,
task_name,
env_wrappers=env_wrappers)
actor_net = actor_rnn_network.ActorRnnNetwork(
tf_env.time_step_spec().observation,
tf_env.action_spec(),
input_fc_layer_params=actor_fc_layers,
lstm_size=actor_lstm_size,
output_fc_layer_params=actor_output_fc_layers)
critic_net_input_specs = (tf_env.time_step_spec().observation,
tf_env.action_spec())
critic_net = critic_rnn_network.CriticRnnNetwork(
critic_net_input_specs,
observation_fc_layer_params=critic_obs_fc_layers,
action_fc_layer_params=critic_action_fc_layers,
joint_fc_layer_params=critic_joint_fc_layers,
lstm_size=critic_lstm_size,
output_fc_layer_params=critic_output_fc_layers,
)
global_step = tf.compat.v1.train.get_or_create_global_step()
tf_agent = td3_agent.Td3Agent(
tf_env.time_step_spec(),
tf_env.action_spec(),
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_learning_rate),
exploration_noise_std=exploration_noise_std,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
actor_update_period=actor_update_period,
dqda_clipping=dqda_clipping,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
debug_summaries=debug_summaries,
train_step_counter=global_step)
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
tf_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=replay_buffer_capacity)
eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy)
train_metrics = [
tf_metrics.NumberOfEpisodes(),
tf_metrics.EnvironmentSteps(),
tf_metrics.AverageReturnMetric(),
tf_metrics.AverageEpisodeLengthMetric(),
]
collect_policy = tf_agent.collect_policy
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
initial_collect_op = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_episodes=initial_collect_steps).run(policy_state=policy_state)
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
collect_op = dynamic_episode_driver.DynamicEpisodeDriver(
tf_env,
collect_policy,
observers=[replay_buffer.add_batch] + train_metrics,
num_episodes=collect_episodes_per_iteration).run(
policy_state=policy_state)
# Need extra step to generate transitions of train_sequence_length.
# Dataset generates trajectories with shape [BxTx...]
dataset = replay_buffer.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=train_sequence_length +
1).prefetch(3)
iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
trajectories, unused_info = iterator.get_next()
train_fn = common.function(tf_agent.train)
train_op = train_fn(experience=trajectories)
train_checkpointer = common.Checkpointer(
ckpt_dir=train_dir,
agent=tf_agent,
global_step=global_step,
metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
train_dir, 'policy'),
policy=tf_agent.policy,
global_step=global_step)
rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
train_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer)
summary_ops = []
for train_metric in train_metrics:
summary_ops.append(
train_metric.tf_summaries(train_step=global_step,
step_metrics=train_metrics[:2]))
with eval_summary_writer.as_default(), \
tf.compat.v2.summary.record_if(True):
for eval_metric in eval_metrics:
eval_metric.tf_summaries(train_step=global_step)
init_agent_op = tf_agent.initialize()
with tf.compat.v1.Session() as sess:
# Initialize the graph.
train_checkpointer.initialize_or_restore(sess)
rb_checkpointer.initialize_or_restore(sess)
sess.run(iterator.initializer)
sess.run(init_agent_op)
sess.run(train_summary_writer.init())
sess.run(eval_summary_writer.init())
sess.run(initial_collect_op)
global_step_val = sess.run(global_step)
metric_utils.compute_summaries(
eval_metrics,
eval_py_env,
eval_py_policy,
num_episodes=num_eval_episodes,
global_step=global_step_val,
callback=eval_metrics_callback,
log=True,
)
collect_call = sess.make_callable(collect_op)
train_step_call = sess.make_callable([train_op, summary_ops])
global_step_call = sess.make_callable(global_step)
timed_at_step = global_step_call()
time_acc = 0
steps_per_second_ph = tf.compat.v1.placeholder(
tf.float32, shape=(), name='steps_per_sec_ph')
steps_per_second_summary = tf.compat.v2.summary.scalar(
name='global_steps_per_sec',
data=steps_per_second_ph,
step=global_step)
for _ in range(num_iterations):
start_time = time.time()
collect_call()
for _ in range(train_steps_per_iteration):
loss_info_value, _ = train_step_call()
time_acc += time.time() - start_time
global_step_val = global_step_call()
if global_step_val % log_interval == 0:
logging.info('step = %d, loss = %f', global_step_val,
loss_info_value.loss)
steps_per_sec = (global_step_val -
timed_at_step) / time_acc
logging.info('%.3f steps/sec', steps_per_sec)
sess.run(steps_per_second_summary,
feed_dict={steps_per_second_ph: steps_per_sec})
timed_at_step = global_step_val
time_acc = 0
if global_step_val % train_checkpoint_interval == 0:
train_checkpointer.save(global_step=global_step_val)
if global_step_val % policy_checkpoint_interval == 0:
policy_checkpointer.save(global_step=global_step_val)
if global_step_val % rb_checkpoint_interval == 0:
rb_checkpointer.save(global_step=global_step_val)
if global_step_val % eval_interval == 0:
metric_utils.compute_summaries(
eval_metrics,
eval_py_env,
eval_py_policy,
num_episodes=num_eval_episodes,
global_step=global_step_val,
callback=eval_metrics_callback,
log=True,
)
def train_eval(
root_dir,
env_name='CartPole-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,
)
