def test_push_under_distribute_strategy(
self, strategy: tf.distribute.Strategy) -> None:
# Prepare nested variables under strategy scope to push into the server.
with strategy.scope():
variables = _create_nested_variable()
logging.info('Variables: %s', variables)
# Push the input to the server.
variable_container = reverb_variable_container.ReverbVariableContainer(
self._server_address)
variable_container.push(variables) # pytype: disable=wrong-arg-types
# Check the content of the server.
self._assert_nested_variable_in_server()
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)