def test_deterministic_dataset_from_heap_sampler_remover(self):
uniform_sampler_min_heap_remover_table = reverb.Table(
name=self._table_name,
sampler=reverb.selectors.MaxHeap(),
remover=reverb.selectors.MinHeap(),
max_size=100,
max_times_sampled=0,
rate_limiter=reverb.rate_limiters.MinSize(1))
server = reverb.Server([uniform_sampler_min_heap_remover_table])
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec,
self._table_name,
local_server=server,
sequence_length=None)
replay.as_dataset(single_deterministic_pass=True)
server.stop()
def test_capacity_set(self):
table_name = 'test_table'
capacity = 100
uniform_table = reverb.Table(
table_name,
max_size=capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(3))
server = reverb.Server([uniform_table])
data_spec = tensor_spec.TensorSpec((), tf.float32)
replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec, table_name, local_server=server, sequence_length=None)
self.assertEqual(capacity, replay.capacity)
server.stop()
def test_experimental_distribute_datasets_from_function(self, strategy):
sequence_length = 3
batch_size = 10
self._insert_random_data(self._env,
num_steps=sequence_length,
sequence_length=sequence_length)
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec,
self._table_name,
sequence_length=sequence_length,
local_server=self._server)
num_replicas = strategy.num_replicas_in_sync
with strategy.scope():
dataset = strategy.experimental_distribute_datasets_from_function(
lambda _: replay.as_dataset(batch_size // num_replicas))
iterator = iter(dataset)
@common.function()
def train_step():
with strategy.scope():
sample, _ = next(iterator)
_, step = sample.observation
loss = strategy.run(lambda x: tf.reduce_mean(x, axis=-1),
args=(step, ))
return strategy.reduce(tf.distribute.ReduceOp.SUM,
loss,
axis=0)
# Test running eagerly
for _ in range(5):
with strategy.scope():
loss = train_step()
self.assertEqual(batch_size, loss)
# Test with wrapping into a tf.function
train_step_fn = common.function(train_step)
for _ in range(5):
with strategy.scope():
loss = train_step_fn()
self.assertEqual(batch_size, loss)
def test_dataset_with_preprocess(self):
def validate_data_observer(traj):
if not array_spec.check_arrays_nest(traj, self._array_data_spec):
raise ValueError('Trajectory incompatible with array_data_spec')
def preprocess(traj):
episode, step = traj.observation
return traj.replace(observation=(episode, step + 1))
# Observe 10 steps from the env. This isn't the num_steps we're testing.
self._insert_random_data(
self._env,
num_steps=10,
additional_observers=[validate_data_observer],
sequence_length=4)
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec,
self._table_name,
local_server=self._server,
sequence_length=4)
dataset = replay.as_dataset(num_steps=2)
for sample, _ in dataset.take(5):
episode, step = sample.observation
self.assertEqual(episode[0], episode[1])
self.assertEqual(step[0] + 1, step[1])
# From even to odd steps
self.assertEqual(0, step[0].numpy() % 2)
self.assertEqual(1, step[1].numpy() % 2)
dataset = replay.as_dataset(
num_steps=2, sample_batch_size=1, sequence_preprocess_fn=preprocess)
for sample, _ in dataset.take(5):
episode, step = sample.observation
self.assertEqual(episode[0, 0], episode[0, 1])
self.assertEqual(step[0, 0] + 1, step[0, 1])
# Makes sure the preprocess has happened.
# From odd to even steps
self.assertEqual(1, step[0, 0].numpy() % 2)
self.assertEqual(0, step[0, 1].numpy() % 2)
def test_single_episode_dataset(self):
sequence_length = 3
self._insert_random_data(self._env,
num_steps=sequence_length,
sequence_length=sequence_length)
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec,
self._table_name,
sequence_length=None,
local_server=self._server)
# Make sure observations are off by 1 given we are counting transitions in
# the env observations.
dataset = replay.as_dataset()
for sample, _ in dataset.take(5):
episode, step = sample.observation
self.assertEqual((sequence_length, ), episode.shape)
self.assertEqual((sequence_length, ), step.shape)
self.assertAllEqual([0] * sequence_length, episode - episode[:1])
self.assertAllEqual(list(range(sequence_length)), step - step[:1])
def test_uniform_table(self):
table_name = 'test_uniform_table'
queue_table = reverb.Table(
table_name,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
max_size=1000,
rate_limiter=reverb.rate_limiters.MinSize(3))
reverb_server = reverb.Server([queue_table])
data_spec = tensor_spec.TensorSpec((), dtype=tf.int64)
replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec,
table_name,
local_server=reverb_server,
sequence_length=1,
dataset_buffer_size=1)
with replay.py_client.trajectory_writer(
num_keep_alive_refs=1) as writer:
for i in range(3):
writer.append(i)
trajectory = writer.history[-1:]
writer.create_item(table_name,
trajectory=trajectory,
priority=1)
dataset = replay.as_dataset(sample_batch_size=1,
num_steps=None,
num_parallel_calls=1)
iterator = iter(dataset)
counts = [0] * 3
for i in range(1000):
item_0 = next(iterator)[0].numpy() # This is a matrix shaped 1x1.
counts[int(item_0)] += 1
# Comparing against 200 to avoid flakyness
self.assertGreater(counts[0], 200)
self.assertGreater(counts[1], 200)
self.assertGreater(counts[2], 200)
def test_dataset_samples_sequential(self, sequence_length):
def validate_data_observer(traj):
if not array_spec.check_arrays_nest(traj, self._array_data_spec):
raise ValueError('Trajectory incompatible with array_data_spec')
# Observe 20 steps from the env. This isn't the num_steps we're testing.
self._insert_random_data(
self._env, num_steps=20,
additional_observers=[validate_data_observer],
sequence_length=sequence_length or 4)
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec, self._table_name, local_server=self._server,
sequence_length=sequence_length)
# Make sure observations belong to the same episode and their step are off
# by 1.
for sample, _ in replay.as_dataset(num_steps=2).take(100):
episode, step = sample.observation
self.assertEqual(episode[0], episode[1])
self.assertEqual(step[0] + 1, step[1])
def test_prioritized_table_max_sample(self):
table_name = 'test_prioritized_table'
table = reverb.Table(table_name,
sampler=reverb.selectors.Prioritized(1.0),
remover=reverb.selectors.Fifo(),
max_times_sampled=10,
rate_limiter=reverb.rate_limiters.MinSize(1),
max_size=3)
reverb_server = reverb.Server([table])
data_spec = tensor_spec.TensorSpec((), dtype=tf.int64)
replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec,
table_name,
sequence_length=1,
local_server=reverb_server,
dataset_buffer_size=1)
with replay.py_client.trajectory_writer(1) as writer:
for i in range(3):
writer.append(i)
writer.create_item(table_name,
trajectory=writer.history[-1:],
priority=i)
dataset = replay.as_dataset(sample_batch_size=3, num_parallel_calls=3)
self.assertTrue(table.can_sample(3))
iterator = iter(dataset)
counts = [0] * 3
for i in range(10):
item_0 = next(iterator)[0].numpy() # This is a matrix shaped 1x3.
for item in item_0:
counts[int(item)] += 1
self.assertFalse(table.can_sample(3))
# Same number of counts due to limit on max_times_sampled
self.assertEqual(counts[0], 10) # priority 0
self.assertEqual(counts[1], 10) # priority 1
self.assertEqual(counts[2], 10) # priority 2
def test_prioritized_table(self):
table_name = 'test_prioritized_table'
queue_table = reverb.Table(
table_name,
sampler=reverb.selectors.Prioritized(1.0),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
max_size=3)
reverb_server = reverb.Server([queue_table])
data_spec = tensor_spec.TensorSpec((), dtype=tf.int64)
replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec,
table_name,
sequence_length=1,
local_server=reverb_server,
dataset_buffer_size=1)
with replay.py_client.writer(max_sequence_length=1) as writer:
for i in range(3):
writer.append(i)
writer.create_item(table=table_name,
num_timesteps=1,
priority=i)
dataset = replay.as_dataset(sample_batch_size=1,
num_steps=None,
num_parallel_calls=None)
iterator = iter(dataset)
counts = [0] * 3
for i in range(1000):
item_0 = next(iterator)[0].numpy() # This is a matrix shaped 1x1.
counts[int(item_0)] += 1
self.assertEqual(counts[0], 0) # priority 0
self.assertGreater(counts[1], 250) # priority 1
self.assertGreater(counts[2], 600) # priority 2
def test_batched_episodes_dataset(self, sequence_length):
# Observe batch_size * sequence_length steps to have at least 3 episodes
batch_size = 3
env = test_envs.EpisodeCountingEnv(steps_per_episode=sequence_length)
self._insert_random_data(env,
num_steps=batch_size * sequence_length,
sequence_length=sequence_length)
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec,
self._table_name,
sequence_length=None,
local_server=self._server)
dataset = replay.as_dataset(batch_size)
for sample, _ in dataset.take(5):
episode, step = sample.observation
self.assertEqual((batch_size, sequence_length), episode.shape)
self.assertEqual((batch_size, sequence_length), step.shape)
for n in range(sequence_length):
# All elements in the same batch should belong to the same episode.
self.assertAllEqual(episode[:, 0], episode[:, n])
# All elements in the same batch should have consecutive steps.
self.assertAllEqual(step[:, 0] + n, step[:, n])
def train(
root_dir: Text,
environment_name: Text,
strategy: tf.distribute.Strategy,
replay_buffer_server_address: Text,
variable_container_server_address: Text,
suite_load_fn: Callable[[Text],
py_environment.PyEnvironment] = suite_mujoco.load,
# Training params
learning_rate: float = 3e-4,
batch_size: int = 256,
num_iterations: int = 2000000,
learner_iterations_per_call: int = 1) -> None:
"""Trains a DQN agent."""
# Get the specs from the environment.
logging.info('Training SAC with learning rate: %f', learning_rate)
env = suite_load_fn(environment_name)
observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(env))
# Create the agent.
with strategy.scope():
train_step = train_utils.create_train_step()
agent = _create_agent(
train_step=train_step,
observation_tensor_spec=observation_tensor_spec,
action_tensor_spec=action_tensor_spec,
time_step_tensor_spec=time_step_tensor_spec,
learning_rate=learning_rate)
# Create the policy saver which saves the initial model now, then it
# periodically checkpoints the policy weigths.
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
save_model_trigger = triggers.PolicySavedModelTrigger(
saved_model_dir, agent, train_step, interval=1000)
# Create the variable container.
variables = {
reverb_variable_container.POLICY_KEY: agent.collect_policy.variables(),
reverb_variable_container.TRAIN_STEP_KEY: train_step
}
variable_container = reverb_variable_container.ReverbVariableContainer(
variable_container_server_address,
table_names=[reverb_variable_container.DEFAULT_TABLE])
variable_container.push(variables)
# Create the replay buffer.
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=reverb_replay_buffer.DEFAULT_TABLE,
server_address=replay_buffer_server_address)
# Initialize the dataset.
def experience_dataset_fn():
with strategy.scope():
return reverb_replay.as_dataset(
sample_batch_size=batch_size, num_steps=2).prefetch(3)
# Create the learner.
learning_triggers = [
save_model_trigger,
triggers.StepPerSecondLogTrigger(train_step, interval=1000)
]
sac_learner = learner.Learner(
root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers,
strategy=strategy)
# Run the training loop.
while train_step.numpy() < num_iterations:
sac_learner.run(iterations=learner_iterations_per_call)
variable_container.push(variables)
def train_eval(
root_dir,
env_name='CartPole-v0',
# Training params
initial_collect_steps=1000,
num_iterations=100000,
fc_layer_params=(100, ),
# Agent params
epsilon_greedy=0.1,
batch_size=64,
learning_rate=1e-3,
n_step_update=1,
gamma=0.99,
target_update_tau=0.05,
target_update_period=5,
reward_scale_factor=1.0,
# Replay params
reverb_port=None,
replay_capacity=100000,
# Others
policy_save_interval=1000,
eval_interval=1000,
eval_episodes=10):
"""Trains and evaluates DQN."""
collect_env = suite_gym.load(env_name)
eval_env = suite_gym.load(env_name)
time_step_tensor_spec = tensor_spec.from_spec(collect_env.time_step_spec())
action_tensor_spec = tensor_spec.from_spec(collect_env.action_spec())
train_step = train_utils.create_train_step()
num_actions = action_tensor_spec.maximum - action_tensor_spec.minimum + 1
# Define a helper function to create Dense layers configured with the right
# activation and kernel initializer.
def dense_layer(num_units):
return tf.keras.layers.Dense(
num_units,
activation=tf.keras.activations.relu,
kernel_initializer=tf.keras.initializers.VarianceScaling(
scale=2.0, mode='fan_in', distribution='truncated_normal'))
# QNetwork consists of a sequence of Dense layers followed by a dense layer
# with `num_actions` units to generate one q_value per available action as
# it's output.
dense_layers = [dense_layer(num_units) for num_units in fc_layer_params]
q_values_layer = tf.keras.layers.Dense(
num_actions,
activation=None,
kernel_initializer=tf.keras.initializers.RandomUniform(minval=-0.03,
maxval=0.03),
bias_initializer=tf.keras.initializers.Constant(-0.2))
q_net = sequential.Sequential(dense_layers + [q_values_layer])
agent = dqn_agent.DqnAgent(
time_step_tensor_spec,
action_tensor_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.keras.optimizers.Adam(learning_rate=learning_rate),
td_errors_loss_fn=common.element_wise_squared_loss,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
train_step_counter=train_step)
table_name = 'uniform_table'
table = reverb.Table(table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_server = reverb.Server([table], port=reverb_port)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=2,
stride_length=1)
dataset = reverb_replay.as_dataset(num_parallel_calls=3,
sample_batch_size=batch_size,
num_steps=2).prefetch(3)
experience_dataset_fn = lambda: dataset
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY:
env_step_metric}),
triggers.StepPerSecondLogTrigger(train_step, interval=100),
]
dqn_learner = learner.Learner(root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
# If we haven't trained yet make sure we collect some random samples first to
# fill up the Replay Buffer with some experience.
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_collect_policy,
use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=1,
observers=[rb_observer, env_step_metric],
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
)
tf_greedy_policy = agent.policy
greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_greedy_policy,
use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
dqn_learner.run(iterations=1)
if eval_interval and dqn_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
def get_reverb_buffer(data_spec,
sequence_length=None,
table_name='uniform_table',
table=None,
reverb_server_address=None,
port=None,
replay_capacity=1000,
min_size_limiter_size=1):
"""Returns an instance of Reverb replay buffer and observer to add items.
Either creates a local reverb server or uses a remote reverb server at
reverb_sever_address (if set).
If reverb_server_address is None, creates a local server with a uniform
table underneath.
Args:
data_spec: spec of the data elements to be stored in the replay buffer
sequence_length: integer specifying sequence_lenghts used to write
to the given table.
table_name: Name of the table to create.
table: Optional table for the backing local server. If None, automatically
creates a uniform sampling table.
reverb_server_address: Address of the remote reverb server, if None a local
server is created.
port: Port to launch the server in.
replay_capacity: Optinal (for default uniform sampling table
only, i.e if table=None) capacity of the uniform sampling table for the
local replay server.
min_size_limiter_size: Optional (for default uniform sampling
table only, i.e if table=None) minimum number of items
required in the RB before sampling can begin, used for local server only.
Returns:
Reverb replay buffer instance
Note: the if local server is created, it is not returned. It can be
retrieved by calling local_server() on the returned replay buffer.
"""
table_signature = tensor_spec.add_outer_dim(data_spec, sequence_length)
if reverb_server_address is None:
if table is None:
table = _create_uniform_table(
table_name,
table_signature,
table_capacity=replay_capacity,
min_size_limiter_size=min_size_limiter_size)
reverb_server = reverb.Server([table], port=port)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec,
sequence_length=sequence_length,
table_name=table_name,
local_server=reverb_server)
else:
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
data_spec,
sequence_length=sequence_length,
table_name=table_name,
server_address=reverb_server_address)
return reverb_replay
def train_agent(iterations, modeldir, logdir, policydir):
"""Train and convert the model using TF Agents."""
# TODO: add code to instantiate the training and evaluation environments
# TODO: add code to create a reinforcement learning agent that is going to be trained
tf_agent.initialize()
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
tf_policy_saver = policy_saver.PolicySaver(collect_policy)
# Use reverb as replay buffer
replay_buffer_signature = tensor_spec.from_spec(tf_agent.collect_data_spec)
table = reverb.Table(
REPLAY_BUFFER_TABLE_NAME,
max_size=REPLAY_BUFFER_CAPACITY,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
signature=replay_buffer_signature,
) # specify signature here for validation at insertion time
reverb_server = reverb.Server([table])
replay_buffer = reverb_replay_buffer.ReverbReplayBuffer(
tf_agent.collect_data_spec,
sequence_length=None,
table_name=REPLAY_BUFFER_TABLE_NAME,
local_server=reverb_server,
)
replay_buffer_observer = reverb_utils.ReverbAddEpisodeObserver(
replay_buffer.py_client, REPLAY_BUFFER_TABLE_NAME,
REPLAY_BUFFER_CAPACITY)
# Optimize by wrapping some of the code in a graph using TF function.
tf_agent.train = common.function(tf_agent.train)
# Evaluate the agent's policy once before training.
avg_return = compute_avg_return_and_steps(eval_env, tf_agent.policy,
NUM_EVAL_EPISODES)
summary_writer = tf.summary.create_file_writer(logdir)
for i in range(iterations):
# TODO: add code to collect game episodes and train the agent
logger = tf.get_logger()
if i % EVAL_INTERVAL == 0:
avg_return, avg_episode_length = compute_avg_return_and_steps(
eval_env, eval_policy, NUM_EVAL_EPISODES)
with summary_writer.as_default():
tf.summary.scalar("Average return", avg_return, step=i)
tf.summary.scalar("Average episode length",
avg_episode_length,
step=i)
summary_writer.flush()
logger.info(
"iteration = {0}: Average Return = {1}, Average Episode Length = {2}"
.format(i, avg_return, avg_episode_length))
summary_writer.close()
tf_policy_saver.save(policydir)
def test_size_empty(self):
replay = reverb_replay_buffer.ReverbReplayBuffer(
self._data_spec, self._table_name, local_server=self._server,
sequence_length=None)
self.assertEqual(replay.num_frames(), 0)
def train_eval(
root_dir,
env_name='Pong-v0',
# Training params
update_frequency=4, # Number of collect steps per policy update
initial_collect_steps=50000, # 50k collect steps
num_iterations=50000000, # 50M collect steps
# Taken from Rainbow as it's not specified in Mnih,15.
max_episode_frames_collect=50000, # env frames observed by the agent
max_episode_frames_eval=108000, # env frames observed by the agent
# Agent params
epsilon_greedy=0.1,
epsilon_decay_period=250000, # 1M collect steps / update_frequency
batch_size=32,
learning_rate=0.00025,
n_step_update=1,
gamma=0.99,
target_update_tau=1.0,
target_update_period=2500, # 10k collect steps / update_frequency
reward_scale_factor=1.0,
# Replay params
reverb_port=None,
replay_capacity=1000000,
# Others
policy_save_interval=250000,
eval_interval=1000,
eval_episodes=30,
debug_summaries=True):
"""Trains and evaluates DQN."""
collect_env = suite_atari.load(
env_name,
max_episode_steps=max_episode_frames_collect,
gym_env_wrappers=suite_atari.DEFAULT_ATARI_GYM_WRAPPERS_WITH_STACKING)
eval_env = suite_atari.load(
env_name,
max_episode_steps=max_episode_frames_eval,
gym_env_wrappers=suite_atari.DEFAULT_ATARI_GYM_WRAPPERS_WITH_STACKING)
unused_observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
train_step = train_utils.create_train_step()
num_actions = action_tensor_spec.maximum - action_tensor_spec.minimum + 1
epsilon = tf.compat.v1.train.polynomial_decay(
1.0,
train_step,
epsilon_decay_period,
end_learning_rate=epsilon_greedy)
agent = dqn_agent.DqnAgent(
time_step_tensor_spec,
action_tensor_spec,
q_network=create_q_network(num_actions),
epsilon_greedy=epsilon,
n_step_update=n_step_update,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
optimizer=tf.compat.v1.train.RMSPropOptimizer(
learning_rate=learning_rate,
decay=0.95,
momentum=0.95,
epsilon=0.01,
centered=True),
td_errors_loss_fn=common.element_wise_huber_loss,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
train_step_counter=train_step,
debug_summaries=debug_summaries)
table_name = 'uniform_table'
table = reverb.Table(
table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_server = reverb.Server([table], port=reverb_port)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client, table_name,
sequence_length=2,
stride_length=1)
dataset = reverb_replay.as_dataset(
sample_batch_size=batch_size, num_steps=2).prefetch(3)
experience_dataset_fn = lambda: dataset
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY: env_step_metric}),
triggers.StepPerSecondLogTrigger(train_step, interval=100),
]
dqn_learner = learner.Learner(
root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
# If we haven't trained yet make sure we collect some random samples first to
# fill up the Replay Buffer with some experience.
random_policy = random_py_policy.RandomPyPolicy(collect_env.time_step_spec(),
collect_env.action_spec())
initial_collect_actor = actor.Actor(
collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_collect_policy,
use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=update_frequency,
observers=[rb_observer, env_step_metric],
metrics=actor.collect_metrics(10),
reference_metrics=[env_step_metric],
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
)
tf_greedy_policy = agent.policy
greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_greedy_policy,
use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
reference_metrics=[env_step_metric],
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
dqn_learner.run(iterations=1)
if eval_interval and dqn_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
def train(
root_dir,
strategy,
replay_buffer_server_address,
variable_container_server_address,
create_agent_fn,
create_env_fn,
# Training params
learning_rate=3e-4,
batch_size=256,
num_iterations=32000,
learner_iterations_per_call=100):
"""Trains a DQN agent."""
# Get the specs from the environment.
logging.info('Training SAC with learning rate: %f', learning_rate)
env = create_env_fn()
observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(env))
# Create the agent.
with strategy.scope():
train_step = train_utils.create_train_step()
agent = create_agent_fn(train_step, observation_tensor_spec,
action_tensor_spec, time_step_tensor_spec,
learning_rate)
agent.initialize()
# Create the policy saver which saves the initial model now, then it
# periodically checkpoints the policy weigths.
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
save_model_trigger = triggers.PolicySavedModelTrigger(
saved_model_dir, agent, train_step, interval=1000)
# Create the variable container.
variables = {
reverb_variable_container.POLICY_KEY: agent.collect_policy.variables(),
reverb_variable_container.TRAIN_STEP_KEY: train_step
}
variable_container = reverb_variable_container.ReverbVariableContainer(
variable_container_server_address,
table_names=[reverb_variable_container.DEFAULT_TABLE])
variable_container.push(variables)
# Create the replay buffer.
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=reverb_replay_buffer.DEFAULT_TABLE,
server_address=replay_buffer_server_address)
# Initialize the dataset.
def experience_dataset_fn():
with strategy.scope():
return reverb_replay.as_dataset(
sample_batch_size=batch_size, num_steps=2).prefetch(3)
# Create the learner.
learning_triggers = [
save_model_trigger,
triggers.StepPerSecondLogTrigger(train_step, interval=1000)
]
sac_learner = learner.Learner(
root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers,
strategy=strategy)
# Run the training loop.
# TODO(b/162440911) change the loop use train_step to handle preemptions
for _ in range(num_iterations):
sac_learner.run(iterations=learner_iterations_per_call)
variable_container.push(variables)
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
# Training params
num_iterations=1600,
actor_fc_layers=(64, 64),
value_fc_layers=(64, 64),
learning_rate=3e-4,
collect_sequence_length=2048,
minibatch_size=64,
num_epochs=10,
# Agent params
importance_ratio_clipping=0.2,
lambda_value=0.95,
discount_factor=0.99,
entropy_regularization=0.,
value_pred_loss_coef=0.5,
use_gae=True,
use_td_lambda_return=True,
gradient_clipping=0.5,
value_clipping=None,
# Replay params
reverb_port=None,
replay_capacity=10000,
# Others
policy_save_interval=5000,
summary_interval=1000,
eval_interval=10000,
eval_episodes=100,
debug_summaries=False,
summarize_grads_and_vars=False):
"""Trains and evaluates PPO (Importance Ratio Clipping).
Args:
root_dir: Main directory path where checkpoints, saved_models, and summaries
will be written to.
env_name: Name for the Mujoco environment to load.
num_iterations: The number of iterations to perform collection and training.
actor_fc_layers: List of fully_connected parameters for the actor network,
where each item is the number of units in the layer.
value_fc_layers: : List of fully_connected parameters for the value network,
where each item is the number of units in the layer.
learning_rate: Learning rate used on the Adam optimizer.
collect_sequence_length: Number of steps to take in each collect run.
minibatch_size: Number of elements in each mini batch. If `None`, the entire
collected sequence will be treated as one batch.
num_epochs: Number of iterations to repeat over all collected data per data
collection step. (Schulman,2017) sets this to 10 for Mujoco, 15 for
Roboschool and 3 for Atari.
importance_ratio_clipping: Epsilon in clipped, surrogate PPO objective. For
more detail, see explanation at the top of the doc.
lambda_value: Lambda parameter for TD-lambda computation.
discount_factor: Discount factor for return computation. Default to `0.99`
which is the value used for all environments from (Schulman, 2017).
entropy_regularization: Coefficient for entropy regularization loss term.
Default to `0.0` because no entropy bonus was used in (Schulman, 2017).
value_pred_loss_coef: Multiplier for value prediction loss to balance with
policy gradient loss. Default to `0.5`, which was used for all
environments in the OpenAI baseline implementation. This parameters is
irrelevant unless you are sharing part of actor_net and value_net. In that
case, you would want to tune this coeeficient, whose value depends on the
network architecture of your choice.
use_gae: If True (default False), uses generalized advantage estimation for
computing per-timestep advantage. Else, just subtracts value predictions
from empirical return.
use_td_lambda_return: If True (default False), uses td_lambda_return for
training value function; here: `td_lambda_return = gae_advantage +
value_predictions`. `use_gae` must be set to `True` as well to enable TD
-lambda returns. If `use_td_lambda_return` is set to True while
`use_gae` is False, the empirical return will be used and a warning will
be logged.
gradient_clipping: Norm length to clip gradients.
value_clipping: Difference between new and old value predictions are clipped
to this threshold. Value clipping could be helpful when training
very deep networks. Default: no clipping.
reverb_port: Port for reverb server, if None, use a randomly chosen unused
port.
replay_capacity: The maximum number of elements for the replay buffer. Items
will be wasted if this is smalled than collect_sequence_length.
policy_save_interval: How often, in train_steps, the policy will be saved.
summary_interval: How often to write data into Tensorboard.
eval_interval: How often to run evaluation, in train_steps.
eval_episodes: Number of episodes to evaluate over.
debug_summaries: Boolean for whether to gather debug summaries.
summarize_grads_and_vars: If true, gradient summaries will be written.
"""
collect_env = suite_mujoco.load(env_name)
eval_env = suite_mujoco.load(env_name)
num_environments = 1
observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
# TODO(b/172267869): Remove this conversion once TensorNormalizer stops
# converting float64 inputs to float32.
observation_tensor_spec = tf.TensorSpec(
dtype=tf.float32, shape=observation_tensor_spec.shape)
train_step = train_utils.create_train_step()
actor_net_builder = ppo_actor_network.PPOActorNetwork()
actor_net = actor_net_builder.create_sequential_actor_net(
actor_fc_layers, action_tensor_spec)
value_net = value_network.ValueNetwork(
observation_tensor_spec,
fc_layer_params=value_fc_layers,
kernel_initializer=tf.keras.initializers.Orthogonal())
current_iteration = tf.Variable(0, dtype=tf.int64)
def learning_rate_fn():
# Linearly decay the learning rate.
return learning_rate * (1 - current_iteration / num_iterations)
agent = ppo_clip_agent.PPOClipAgent(
time_step_tensor_spec,
action_tensor_spec,
optimizer=tf.keras.optimizers.Adam(
learning_rate=learning_rate_fn, epsilon=1e-5),
actor_net=actor_net,
value_net=value_net,
importance_ratio_clipping=importance_ratio_clipping,
lambda_value=lambda_value,
discount_factor=discount_factor,
entropy_regularization=entropy_regularization,
value_pred_loss_coef=value_pred_loss_coef,
# This is a legacy argument for the number of times we repeat the data
# inside of the train function, incompatible with mini batch learning.
# We set the epoch number from the replay buffer and tf.Data instead.
num_epochs=1,
use_gae=use_gae,
use_td_lambda_return=use_td_lambda_return,
gradient_clipping=gradient_clipping,
value_clipping=value_clipping,
# TODO(b/150244758): Default compute_value_and_advantage_in_train to False
# after Reverb open source.
compute_value_and_advantage_in_train=False,
# Skips updating normalizers in the agent, as it's handled in the learner.
update_normalizers_in_train=False,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step)
agent.initialize()
reverb_server = reverb.Server(
[
reverb.Table( # Replay buffer storing experience for training.
name='training_table',
sampler=reverb.selectors.Fifo(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
max_size=replay_capacity,
max_times_sampled=1,
),
reverb.Table( # Replay buffer storing experience for normalization.
name='normalization_table',
sampler=reverb.selectors.Fifo(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
max_size=replay_capacity,
max_times_sampled=1,
)
],
port=reverb_port)
# Create the replay buffer.
reverb_replay_train = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=collect_sequence_length,
table_name='training_table',
server_address='localhost:{}'.format(reverb_server.port),
# The only collected sequence is used to populate the batches.
max_cycle_length=1,
rate_limiter_timeout_ms=1000)
reverb_replay_normalization = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=collect_sequence_length,
table_name='normalization_table',
server_address='localhost:{}'.format(reverb_server.port),
# The only collected sequence is used to populate the batches.
max_cycle_length=1,
rate_limiter_timeout_ms=1000)
rb_observer = reverb_utils.ReverbTrajectorySequenceObserver(
reverb_replay_train.py_client, ['training_table', 'normalization_table'],
sequence_length=collect_sequence_length,
stride_length=collect_sequence_length)
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
collect_env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={
triggers.ENV_STEP_METADATA_KEY: collect_env_step_metric
}),
triggers.StepPerSecondLogTrigger(train_step, interval=summary_interval),
]
def training_dataset_fn():
return reverb_replay_train.as_dataset(
sample_batch_size=num_environments,
sequence_preprocess_fn=agent.preprocess_sequence)
def normalization_dataset_fn():
return reverb_replay_normalization.as_dataset(
sample_batch_size=num_environments,
sequence_preprocess_fn=agent.preprocess_sequence)
agent_learner = ppo_learner.PPOLearner(
root_dir,
train_step,
agent,
experience_dataset_fn=training_dataset_fn,
normalization_dataset_fn=normalization_dataset_fn,
num_samples=1,
num_epochs=num_epochs,
minibatch_size=minibatch_size,
shuffle_buffer_size=collect_sequence_length,
triggers=learning_triggers)
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_collect_policy, use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=collect_sequence_length,
observers=[rb_observer],
metrics=actor.collect_metrics(buffer_size=10) + [collect_env_step_metric],
reference_metrics=[collect_env_step_metric],
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
summary_interval=summary_interval)
eval_greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(
agent.policy, use_tf_function=True)
if eval_interval:
logging.info('Intial evaluation.')
eval_actor = actor.Actor(
eval_env,
eval_greedy_policy,
train_step,
metrics=actor.eval_metrics(eval_episodes),
reference_metrics=[collect_env_step_metric],
summary_dir=os.path.join(root_dir, 'eval'),
episodes_per_run=eval_episodes)
eval_actor.run_and_log()
logging.info('Training on %s', env_name)
last_eval_step = 0
for i in range(num_iterations):
collect_actor.run()
rb_observer.flush()
agent_learner.run()
reverb_replay_train.clear()
reverb_replay_normalization.clear()
current_iteration.assign_add(1)
# Eval only if `eval_interval` has been set. Then, eval if the current train
# step is equal or greater than the `last_eval_step` + `eval_interval` or if
# this is the last iteration. This logic exists because agent_learner.run()
# does not return after every train step.
if (eval_interval and
(agent_learner.train_step_numpy >= eval_interval + last_eval_step
or i == num_iterations - 1)):
logging.info('Evaluating.')
eval_actor.run_and_log()
last_eval_step = agent_learner.train_step_numpy
rb_observer.close()
reverb_server.stop()
def train_eval(
root_dir,
env_name,
# Training params
train_sequence_length,
initial_collect_steps=1000,
collect_steps_per_iteration=1,
num_iterations=100000,
# RNN params.
q_network_fn=q_lstm_network, # defaults to q_lstm_network.
# Agent params
epsilon_greedy=0.1,
batch_size=64,
learning_rate=1e-3,
gamma=0.99,
target_update_tau=0.05,
target_update_period=5,
reward_scale_factor=1.0,
# Replay params
reverb_port=None,
replay_capacity=100000,
# Others
policy_save_interval=1000,
eval_interval=1000,
eval_episodes=10):
"""Trains and evaluates DQN."""
collect_env = suite_gym.load(env_name)
eval_env = suite_gym.load(env_name)
unused_observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
train_step = train_utils.create_train_step()
num_actions = action_tensor_spec.maximum - action_tensor_spec.minimum + 1
q_net = q_network_fn(num_actions=num_actions)
sequence_length = train_sequence_length + 1
agent = dqn_agent.DqnAgent(
time_step_tensor_spec,
action_tensor_spec,
q_network=q_net,
epsilon_greedy=epsilon_greedy,
# n-step updates aren't supported with RNNs yet.
n_step_update=1,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
td_errors_loss_fn=common.element_wise_squared_loss,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
train_step_counter=train_step)
table_name = 'uniform_table'
table = reverb.Table(table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_server = reverb.Server([table], port=reverb_port)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=sequence_length,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=sequence_length,
stride_length=1,
pad_end_of_episodes=True)
def experience_dataset_fn():
return reverb_replay.as_dataset(sample_batch_size=batch_size,
num_steps=sequence_length)
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY:
env_step_metric}),
triggers.StepPerSecondLogTrigger(train_step, interval=100),
]
dqn_learner = learner.Learner(root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
# If we haven't trained yet make sure we collect some random samples first to
# fill up the Replay Buffer with some experience.
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_collect_policy,
use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=collect_steps_per_iteration,
observers=[rb_observer, env_step_metric],
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
)
tf_greedy_policy = agent.policy
greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_greedy_policy,
use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
dqn_learner.run(iterations=1)
if eval_interval and dqn_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
table_name = 'uniform_table'
replay_buffer_signature = tensor_spec.from_spec(tf_agent.collect_data_spec)
replay_buffer_signature = tensor_spec.add_outer_dim(replay_buffer_signature)
table = reverb.Table(table_name,
max_size=replay_buffer_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
signature=replay_buffer_signature)
reverb_server = reverb.Server([table])
replay_buffer = reverb_replay_buffer.ReverbReplayBuffer(
tf_agent.collect_data_spec,
table_name=table_name,
sequence_length=None,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddEpisodeObserver(replay_buffer.py_client,
table_name,
replay_buffer_capacity)
def collect_episode(environment, policy, num_episodes):
driver = py_driver.PyDriver(environment,
py_tf_eager_policy.PyTFEagerPolicy(
policy, use_tf_function=True),
[rb_observer],
max_episodes=num_episodes)
def train_agent(iterations, modeldir, logdir, policydir):
"""Train and convert the model using TF Agents."""
train_py_env = planestrike_py_environment.PlaneStrikePyEnvironment(
board_size=BOARD_SIZE, discount=DISCOUNT, max_steps=BOARD_SIZE**2)
eval_py_env = planestrike_py_environment.PlaneStrikePyEnvironment(
board_size=BOARD_SIZE, discount=DISCOUNT, max_steps=BOARD_SIZE**2)
train_env = tf_py_environment.TFPyEnvironment(train_py_env)
eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)
# Alternatively you could use ActorDistributionNetwork as actor_net
actor_net = tfa.networks.Sequential(
[
tfa.keras_layers.InnerReshape([BOARD_SIZE, BOARD_SIZE],
[BOARD_SIZE**2]),
tf.keras.layers.Dense(FC_LAYER_PARAMS, activation='relu'),
tf.keras.layers.Dense(BOARD_SIZE**2),
tf.keras.layers.Lambda(
lambda t: tfp.distributions.Categorical(logits=t)),
],
input_spec=train_py_env.observation_spec())
optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
train_step_counter = tf.Variable(0)
tf_agent = reinforce_agent.ReinforceAgent(
train_env.time_step_spec(),
train_env.action_spec(),
actor_network=actor_net,
optimizer=optimizer,
normalize_returns=True,
train_step_counter=train_step_counter)
tf_agent.initialize()
eval_policy = tf_agent.policy
collect_policy = tf_agent.collect_policy
tf_policy_saver = policy_saver.PolicySaver(collect_policy)
# Use reverb as replay buffer
replay_buffer_signature = tensor_spec.from_spec(tf_agent.collect_data_spec)
table = reverb.Table(
REPLAY_BUFFER_TABLE_NAME,
max_size=REPLAY_BUFFER_CAPACITY,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1),
signature=replay_buffer_signature
) # specify signature here for validation at insertion time
reverb_server = reverb.Server([table])
replay_buffer = reverb_replay_buffer.ReverbReplayBuffer(
tf_agent.collect_data_spec,
sequence_length=None,
table_name=REPLAY_BUFFER_TABLE_NAME,
local_server=reverb_server)
replay_buffer_observer = reverb_utils.ReverbAddEpisodeObserver(
replay_buffer.py_client, REPLAY_BUFFER_TABLE_NAME,
REPLAY_BUFFER_CAPACITY)
# Optimize by wrapping some of the code in a graph using TF function.
tf_agent.train = common.function(tf_agent.train)
# Evaluate the agent's policy once before training.
avg_return = compute_avg_return_and_steps(eval_env, tf_agent.policy,
NUM_EVAL_EPISODES)
summary_writer = tf.summary.create_file_writer(logdir)
for i in range(iterations):
# Collect a few episodes using collect_policy and save to the replay buffer.
collect_episode(train_py_env, collect_policy,
COLLECT_EPISODES_PER_ITERATION, replay_buffer_observer)
# Use data from the buffer and update the agent's network.
iterator = iter(replay_buffer.as_dataset(sample_batch_size=1))
trajectories, _ = next(iterator)
tf_agent.train(experience=trajectories)
replay_buffer.clear()
logger = tf.get_logger()
if i % EVAL_INTERVAL == 0:
avg_return, avg_episode_length = compute_avg_return_and_steps(
eval_env, eval_policy, NUM_EVAL_EPISODES)
with summary_writer.as_default():
tf.summary.scalar('Average return', avg_return, step=i)
tf.summary.scalar('Average episode length',
avg_episode_length,
step=i)
summary_writer.flush()
logger.info(
'iteration = {0}: Average Return = {1}, Average Episode Length = {2}'
.format(i, avg_return, avg_episode_length))
summary_writer.close()
tf_policy_saver.save(policydir)
# Convert to tflite model
converter = tf.lite.TFLiteConverter.from_saved_model(
policydir, signature_keys=['action'])
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_policy = converter.convert()
with open(os.path.join(modeldir, 'planestrike_tf_agents.tflite'),
'wb') as f:
f.write(tflite_policy)
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
# Training params
initial_collect_steps=10000,
num_iterations=3200000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
# Agent params
batch_size=256,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
gamma=0.99,
target_update_tau=0.005,
target_update_period=1,
reward_scale_factor=0.1,
# Replay params
reverb_port=None,
replay_capacity=1000000,
# Others
# Defaults to not checkpointing saved policy. If you wish to enable this,
# please note the caveat explained in README.md.
policy_save_interval=-1,
eval_interval=10000,
eval_episodes=30,
debug_summaries=False,
summarize_grads_and_vars=False):
"""Trains and evaluates SAC."""
logging.info('Training SAC on: %s', env_name)
collect_env = suite_mujoco.load(env_name)
eval_env = suite_mujoco.load(env_name)
observation_tensor_spec, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
train_step = train_utils.create_train_step()
actor_net = actor_distribution_network.ActorDistributionNetwork(
observation_tensor_spec,
action_tensor_spec,
fc_layer_params=actor_fc_layers,
continuous_projection_net=tanh_normal_projection_network.
TanhNormalProjectionNetwork)
critic_net = critic_network.CriticNetwork(
(observation_tensor_spec, action_tensor_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')
agent = sac_agent.SacAgent(
time_step_tensor_spec,
action_tensor_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=tf.math.squared_difference,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=None,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step)
agent.initialize()
table_name = 'uniform_table'
table = reverb.Table(table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_server = reverb.Server([table], port=reverb_port)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=2,
stride_length=1)
dataset = reverb_replay.as_dataset(sample_batch_size=batch_size,
num_steps=2).prefetch(50)
experience_dataset_fn = lambda: dataset
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY:
env_step_metric}),
triggers.StepPerSecondLogTrigger(train_step, interval=1000),
]
agent_learner = learner.Learner(root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_collect_policy,
use_tf_function=True)
collect_actor = actor.Actor(collect_env,
collect_policy,
train_step,
steps_per_run=1,
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(
root_dir, learner.TRAIN_DIR),
observers=[rb_observer, env_step_metric])
tf_greedy_policy = greedy_policy.GreedyPolicy(agent.policy)
eval_greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_greedy_policy, use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
eval_greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
agent_learner.run(iterations=1)
if eval_interval and agent_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
def train_eval(
root_dir,
strategy: tf.distribute.Strategy,
env_name='HalfCheetah-v2',
# Training params
initial_collect_steps=10000,
num_iterations=3200000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
# Agent params
batch_size=256,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
gamma=0.99,
target_update_tau=0.005,
target_update_period=1,
reward_scale_factor=0.1,
# Replay params
reverb_port=None,
replay_capacity=1000000,
# Others
policy_save_interval=10000,
replay_buffer_save_interval=100000,
eval_interval=10000,
eval_episodes=30,
debug_summaries=False,
summarize_grads_and_vars=False):
"""Trains and evaluates SAC."""
logging.info('Training SAC on: %s', env_name)
collect_env = suite_mujoco.load(env_name)
eval_env = suite_mujoco.load(env_name)
_, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
actor_net = create_sequential_actor_network(
actor_fc_layers=actor_fc_layers, action_tensor_spec=action_tensor_spec)
critic_net = create_sequential_critic_network(
obs_fc_layer_units=critic_obs_fc_layers,
action_fc_layer_units=critic_action_fc_layers,
joint_fc_layer_units=critic_joint_fc_layers)
with strategy.scope():
train_step = train_utils.create_train_step()
agent = sac_agent.SacAgent(
time_step_tensor_spec,
action_tensor_spec,
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.keras.optimizers.Adam(
learning_rate=actor_learning_rate),
critic_optimizer=tf.keras.optimizers.Adam(
learning_rate=critic_learning_rate),
alpha_optimizer=tf.keras.optimizers.Adam(
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,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=None,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step)
agent.initialize()
table_name = 'uniform_table'
table = reverb.Table(table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_checkpoint_dir = os.path.join(root_dir, learner.TRAIN_DIR,
learner.REPLAY_BUFFER_CHECKPOINT_DIR)
reverb_checkpointer = reverb.platform.checkpointers_lib.DefaultCheckpointer(
path=reverb_checkpoint_dir)
reverb_server = reverb.Server([table],
port=reverb_port,
checkpointer=reverb_checkpointer)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=2,
stride_length=1)
def experience_dataset_fn():
return reverb_replay.as_dataset(sample_batch_size=batch_size,
num_steps=2).prefetch(50)
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY:
env_step_metric}),
triggers.ReverbCheckpointTrigger(
train_step,
interval=replay_buffer_save_interval,
reverb_client=reverb_replay.py_client),
# TODO(b/165023684): Add SIGTERM handler to checkpoint before preemption.
triggers.StepPerSecondLogTrigger(train_step, interval=1000),
]
agent_learner = learner.Learner(root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers,
strategy=strategy)
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(tf_collect_policy,
use_tf_function=True)
collect_actor = actor.Actor(collect_env,
collect_policy,
train_step,
steps_per_run=1,
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(
root_dir, learner.TRAIN_DIR),
observers=[rb_observer, env_step_metric])
tf_greedy_policy = greedy_policy.GreedyPolicy(agent.policy)
eval_greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_greedy_policy, use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
eval_greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
agent_learner.run(iterations=1)
if eval_interval and agent_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
