class_docstr: Distributional MPO Agent.
  
  This implements a single-process distributional MPO agent. This is an
  actor-critic algorithm that generates data via a behavior policy, inserts
  N-step transitions into a replay buffer, and periodically updates the policy
  (and as a result the behavior) by sampling uniformly from this buffer.
  This agent distinguishes itself from the MPO agent by using a distributional
  critic (state-action value approximator).
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docstr: Initialize the agent.
  
  Args:
    environment_spec: description of the actions, observations, etc.
    policy_network: the online (optimized) policy.
    critic_network: the online critic.
    observation_network: optional network to transform the observations before
      they are fed into any network.
    discount: discount to use for TD updates.
    batch_size: batch size for updates.
    prefetch_size: size to prefetch from replay.
    target_policy_update_period: number of updates to perform before updating
      the target policy network.
    target_critic_update_period: number of updates to perform before updating
      the target critic network.
    min_replay_size: minimum replay size before updating.
    max_replay_size: maximum replay size.
    samples_per_insert: number of samples to take from replay for every insert
      that is made.
    policy_loss_module: configured MPO loss function for the policy
      optimization; defaults to sensible values on the control suite.
      See `acme/tf/losses/mpo.py` for more details.
    policy_optimizer: optimizer to be used on the policy.
    critic_optimizer: optimizer to be used on the critic.
    n_step: number of steps to squash into a single transition.
    num_samples: number of actions to sample when doing a Monte Carlo
      integration with respect to the policy.
    clipping: whether to clip gradients by global norm.
    logger: logging object used to write to logs.
    counter: counter object used to keep track of steps.
    checkpoint: boolean indicating whether to checkpoint the learner.
    replay_table_name: string indicating what name to give the replay table.
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code: def __init__(
      self,
      environment_spec: specs.EnvironmentSpec,
      policy_network: snt.Module,
      critic_network: snt.Module,
      observation_network: types.TensorTransformation = tf.identity,
      discount: float = 0.99,
      batch_size: int = 256,
      prefetch_size: int = 4,
      target_policy_update_period: int = 100,
      target_critic_update_period: int = 100,
      min_replay_size: int = 1000,
      max_replay_size: int = 1000000,
      samples_per_insert: float = 32.0,
      policy_loss_module: Optional[snt.Module] = None,
      policy_optimizer: Optional[snt.Optimizer] = None,
      critic_optimizer: Optional[snt.Optimizer] = None,
      n_step: int = 5,
      num_samples: int = 20,
      clipping: bool = True,
      logger: Optional[loggers.Logger] = None,
      counter: Optional[counting.Counter] = None,
      checkpoint: bool = True,
      replay_table_name: str = adders.DEFAULT_PRIORITY_TABLE,
  ):
      """Initialize the agent.
  
      Args:
        environment_spec: description of the actions, observations, etc.
        policy_network: the online (optimized) policy.
        critic_network: the online critic.
        observation_network: optional network to transform the observations before
          they are fed into any network.
        discount: discount to use for TD updates.
        batch_size: batch size for updates.
        prefetch_size: size to prefetch from replay.
        target_policy_update_period: number of updates to perform before updating
          the target policy network.
        target_critic_update_period: number of updates to perform before updating
          the target critic network.
        min_replay_size: minimum replay size before updating.
        max_replay_size: maximum replay size.
        samples_per_insert: number of samples to take from replay for every insert
          that is made.
        policy_loss_module: configured MPO loss function for the policy
          optimization; defaults to sensible values on the control suite.
          See `acme/tf/losses/mpo.py` for more details.
        policy_optimizer: optimizer to be used on the policy.
        critic_optimizer: optimizer to be used on the critic.
        n_step: number of steps to squash into a single transition.
        num_samples: number of actions to sample when doing a Monte Carlo
          integration with respect to the policy.
        clipping: whether to clip gradients by global norm.
        logger: logging object used to write to logs.
        counter: counter object used to keep track of steps.
        checkpoint: boolean indicating whether to checkpoint the learner.
        replay_table_name: string indicating what name to give the replay table.
      """
  
      # Create a replay server to add data to.
      replay_table = reverb.Table(
          name=adders.DEFAULT_PRIORITY_TABLE,
          sampler=reverb.selectors.Uniform(),
          remover=reverb.selectors.Fifo(),
          max_size=max_replay_size,
          rate_limiter=reverb.rate_limiters.MinSize(
              min_size_to_sample=1
          ),
          signature=adders.NStepTransitionAdder.signature(
              environment_spec
          ),
      )
      self._server = reverb.Server([replay_table], port=None)
  
      # The adder is used to insert observations into replay.
      address = f"localhost:{self._server.port}"
      adder = adders.NStepTransitionAdder(
          client=reverb.Client(address),
          n_step=n_step,
          discount=discount,
      )
  
      # The dataset object to learn from.
      dataset = datasets.make_reverb_dataset(
          table=replay_table_name,
          server_address=address,
          batch_size=batch_size,
          prefetch_size=prefetch_size,
      )
  
      # Make sure observation network is a Sonnet Module.
      observation_network = tf2_utils.to_sonnet_module(
          observation_network
      )
  
      # Create target networks before creating online/target network variables.
      target_policy_network = copy.deepcopy(policy_network)
      target_critic_network = copy.deepcopy(critic_network)
      target_observation_network = copy.deepcopy(observation_network)
  
      # Get observation and action specs.
      act_spec = environment_spec.actions
      obs_spec = environment_spec.observations
      emb_spec = tf2_utils.create_variables(
          observation_network, [obs_spec]
      )
  
      # Create the behavior policy.
      behavior_network = snt.Sequential(
          [
              observation_network,
              policy_network,
              networks.StochasticSamplingHead(),
          ]
      )
  
      # Create variables.
      tf2_utils.create_variables(policy_network, [emb_spec])
      tf2_utils.create_variables(critic_network, [emb_spec, act_spec])
      tf2_utils.create_variables(target_policy_network, [emb_spec])
      tf2_utils.create_variables(
          target_critic_network, [emb_spec, act_spec]
      )
      tf2_utils.create_variables(target_observation_network, [obs_spec])
  
      # Create the actor which defines how we take actions.
      actor = actors.FeedForwardActor(
          policy_network=behavior_network, adder=adder
      )
  
      # Create optimizers.
      policy_optimizer = policy_optimizer or snt.optimizers.Adam(1e-4)
      critic_optimizer = critic_optimizer or snt.optimizers.Adam(1e-4)
  
      # The learner updates the parameters (and initializes them).
      learner = learning.DistributionalMPOLearner(
          policy_network=policy_network,
          critic_network=critic_network,
          observation_network=observation_network,
          target_policy_network=target_policy_network,
          target_critic_network=target_critic_network,
          target_observation_network=target_observation_network,
          policy_loss_module=policy_loss_module,
          policy_optimizer=policy_optimizer,
          critic_optimizer=critic_optimizer,
          clipping=clipping,
          discount=discount,
          num_samples=num_samples,
          target_policy_update_period=target_policy_update_period,
          target_critic_update_period=target_critic_update_period,
          dataset=dataset,
          logger=logger,
          counter=counter,
          checkpoint=checkpoint,
      )
  
      super().__init__(
          actor=actor,
          learner=learner,
          min_observations=max(batch_size, min_replay_size),
          observations_per_step=float(batch_size) / samples_per_insert,
      )
  
file_path: acme/acme/agents/tf/dmpo/agent.py
imports: import copy
  from typing import Optional
  from acme import datasets
  from acme import specs
  from acme import types
  from acme.adders import reverb
  from acme.agents import agent
  from acme.agents.tf import actors
  from acme.agents.tf.dmpo import learning
  from acme.tf import networks
  from acme.tf import utils
  from acme.utils import counting
  from acme.utils import loggers
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 1 to create a new DistributionalMPO object from inherited base classes: agent with example: obj = DistributionalMPO(environment_spec, policy_network, critic_network, observation_network, discount, batch_size, prefetch_size, target_policy_update_period, target_critic_update_period, min_replay_size, max_replay_size, samples_per_insert, policy_loss_module, policy_optimizer, critic_optimizer, n_step, num_samples, clipping, logger, counter, checkpoint, replay_table_name)