class_docstr: A SAC Agent.
class_docstr_tok: ["A","SAC","Agent","."]
docstr: Creates a SAC Agent.
  
  Args:
    time_step_spec: A `TimeStep` spec of the expected time_steps.
    action_spec: A nest of BoundedTensorSpec representing the actions.
    critic_network: A function critic_network((observations, actions)) that
      returns the q_values for each observation and action.
    actor_network: A function actor_network(observation, action_spec) that
      returns action distribution.
    actor_optimizer: The optimizer to use for the actor network.
    critic_optimizer: The default optimizer to use for the critic network.
    alpha_optimizer: The default optimizer to use for the alpha variable.
    actor_loss_weight: The weight on actor loss.
    critic_loss_weight: The weight on critic loss.
    alpha_loss_weight: The weight on alpha loss.
    actor_policy_ctor: The policy class to use.
    critic_network_2: (Optional.)  A `tf_agents.network.Network` to be used as
      the second critic network during Q learning.  The weights from
      `critic_network` are copied if this is not provided.
    target_critic_network: (Optional.)  A `tf_agents.network.Network` to be
      used as the target critic network during Q learning. Every
      `target_update_period` train steps, the weights from `critic_network`
      are copied (possibly withsmoothing via `target_update_tau`) to `
      target_critic_network`.  If `target_critic_network` is not provided, it
      is created by making a copy of `critic_network`, which initializes a new
      network with the same structure and its own layers and weights.
      Performing a `Network.copy` does not work when the network instance
      already has trainable parameters (e.g., has already been built, or when
      the network is sharing layers with another).  In these cases, it is up
      to you to build a copy having weights that are not shared with the
      original `critic_network`, so that this can be used as a target network.
      If you provide a `target_critic_network` that shares any weights with
      `critic_network`, a warning will be logged but no exception is thrown.
    target_critic_network_2: (Optional.) Similar network as
      target_critic_network but for the critic_network_2. See documentation
      for target_critic_network. Will only be used if 'critic_network_2' is
      also specified.
    target_update_tau: Factor for soft update of the target networks.
    target_update_period: Period for soft update of the target networks.
    td_errors_loss_fn:  A function for computing the elementwise TD errors
      loss.
    gamma: A discount factor for future rewards.
    reward_scale_factor: Multiplicative scale for the reward.
    initial_log_alpha: Initial value for log_alpha.
    use_log_alpha_in_alpha_loss: A boolean, whether using log_alpha or alpha
      in alpha loss. Certain implementations of SAC use log_alpha as log
      values are generally nicer to work with.
    target_entropy: The target average policy entropy, for updating alpha. The
      default value is negative of the total number of actions.
    gradient_clipping: Norm length to clip gradients.
    debug_summaries: A bool to gather debug summaries.
    summarize_grads_and_vars: If True, gradient and network variable summaries
      will be written during training.
    train_step_counter: An optional counter to increment every time the train
      op is run.  Defaults to the global_step.
    name: The name of this agent. All variables in this module will fall under
      that name. Defaults to the class name.
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code: def __init__(
      self,
      time_step_spec: ts.TimeStep,
      action_spec: types.NestedTensorSpec,
      critic_network: network.Network,
      actor_network: network.Network,
      actor_optimizer: types.Optimizer,
      critic_optimizer: types.Optimizer,
      alpha_optimizer: types.Optimizer,
      actor_loss_weight: types.Float = 1.0,
      critic_loss_weight: types.Float = 0.5,
      alpha_loss_weight: types.Float = 1.0,
      actor_policy_ctor: Callable[
          ..., tf_policy.TFPolicy
      ] = actor_policy.ActorPolicy,
      critic_network_2: Optional[network.Network] = None,
      target_critic_network: Optional[network.Network] = None,
      target_critic_network_2: Optional[network.Network] = None,
      target_update_tau: types.Float = 1.0,
      target_update_period: types.Int = 1,
      td_errors_loss_fn: types.LossFn = tf.math.squared_difference,
      gamma: types.Float = 1.0,
      reward_scale_factor: types.Float = 1.0,
      initial_log_alpha: types.Float = 0.0,
      use_log_alpha_in_alpha_loss: bool = True,
      target_entropy: Optional[types.Float] = None,
      gradient_clipping: Optional[types.Float] = None,
      debug_summaries: bool = False,
      summarize_grads_and_vars: bool = False,
      train_step_counter: Optional[tf.Variable] = None,
      name: Optional[Text] = None,
  ):
      """Creates a SAC Agent.
  
      Args:
        time_step_spec: A `TimeStep` spec of the expected time_steps.
        action_spec: A nest of BoundedTensorSpec representing the actions.
        critic_network: A function critic_network((observations, actions)) that
          returns the q_values for each observation and action.
        actor_network: A function actor_network(observation, action_spec) that
          returns action distribution.
        actor_optimizer: The optimizer to use for the actor network.
        critic_optimizer: The default optimizer to use for the critic network.
        alpha_optimizer: The default optimizer to use for the alpha variable.
        actor_loss_weight: The weight on actor loss.
        critic_loss_weight: The weight on critic loss.
        alpha_loss_weight: The weight on alpha loss.
        actor_policy_ctor: The policy class to use.
        critic_network_2: (Optional.)  A `tf_agents.network.Network` to be used as
          the second critic network during Q learning.  The weights from
          `critic_network` are copied if this is not provided.
        target_critic_network: (Optional.)  A `tf_agents.network.Network` to be
          used as the target critic network during Q learning. Every
          `target_update_period` train steps, the weights from `critic_network`
          are copied (possibly withsmoothing via `target_update_tau`) to `
          target_critic_network`.  If `target_critic_network` is not provided, it
          is created by making a copy of `critic_network`, which initializes a new
          network with the same structure and its own layers and weights.
          Performing a `Network.copy` does not work when the network instance
          already has trainable parameters (e.g., has already been built, or when
          the network is sharing layers with another).  In these cases, it is up
          to you to build a copy having weights that are not shared with the
          original `critic_network`, so that this can be used as a target network.
          If you provide a `target_critic_network` that shares any weights with
          `critic_network`, a warning will be logged but no exception is thrown.
        target_critic_network_2: (Optional.) Similar network as
          target_critic_network but for the critic_network_2. See documentation
          for target_critic_network. Will only be used if 'critic_network_2' is
          also specified.
        target_update_tau: Factor for soft update of the target networks.
        target_update_period: Period for soft update of the target networks.
        td_errors_loss_fn:  A function for computing the elementwise TD errors
          loss.
        gamma: A discount factor for future rewards.
        reward_scale_factor: Multiplicative scale for the reward.
        initial_log_alpha: Initial value for log_alpha.
        use_log_alpha_in_alpha_loss: A boolean, whether using log_alpha or alpha
          in alpha loss. Certain implementations of SAC use log_alpha as log
          values are generally nicer to work with.
        target_entropy: The target average policy entropy, for updating alpha. The
          default value is negative of the total number of actions.
        gradient_clipping: Norm length to clip gradients.
        debug_summaries: A bool to gather debug summaries.
        summarize_grads_and_vars: If True, gradient and network variable summaries
          will be written during training.
        train_step_counter: An optional counter to increment every time the train
          op is run.  Defaults to the global_step.
        name: The name of this agent. All variables in this module will fall under
          that name. Defaults to the class name.
      """
      tf.Module.__init__(self, name=name)
  
      self._check_action_spec(action_spec)
  
      net_observation_spec = time_step_spec.observation
      critic_spec = (net_observation_spec, action_spec)
  
      self._critic_network_1 = critic_network
  
      if critic_network_2 is not None:
          self._critic_network_2 = critic_network_2
      else:
          self._critic_network_2 = critic_network.copy(
              name="CriticNetwork2"
          )
          # Do not use target_critic_network_2 if critic_network_2 is None.
          target_critic_network_2 = None
  
      # Wait until critic_network_2 has been copied from critic_network_1 before
      # creating variables on both.
      self._critic_network_1.create_variables(critic_spec)
      self._critic_network_2.create_variables(critic_spec)
  
      if target_critic_network:
          target_critic_network.create_variables(critic_spec)
  
      self._target_critic_network_1 = (
          common.maybe_copy_target_network_with_checks(
              self._critic_network_1,
              target_critic_network,
              input_spec=critic_spec,
              name="TargetCriticNetwork1",
          )
      )
  
      if target_critic_network_2:
          target_critic_network_2.create_variables(critic_spec)
      self._target_critic_network_2 = (
          common.maybe_copy_target_network_with_checks(
              self._critic_network_2,
              target_critic_network_2,
              input_spec=critic_spec,
              name="TargetCriticNetwork2",
          )
      )
  
      if actor_network:
          actor_network.create_variables(net_observation_spec)
      self._actor_network = actor_network
  
      policy = actor_policy_ctor(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          actor_network=self._actor_network,
          training=False,
      )
  
      self._train_policy = actor_policy_ctor(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          actor_network=self._actor_network,
          training=True,
      )
  
      self._log_alpha = common.create_variable(
          "initial_log_alpha",
          initial_value=initial_log_alpha,
          dtype=tf.float32,
          trainable=True,
      )
  
      if target_entropy is None:
          target_entropy = self._get_default_target_entropy(action_spec)
  
      self._use_log_alpha_in_alpha_loss = use_log_alpha_in_alpha_loss
      self._target_update_tau = target_update_tau
      self._target_update_period = target_update_period
      self._actor_optimizer = self._convert_optimizer_to_legacy(
          actor_optimizer
      )
      self._critic_optimizer = self._convert_optimizer_to_legacy(
          critic_optimizer
      )
      self._alpha_optimizer = self._convert_optimizer_to_legacy(
          alpha_optimizer
      )
      self._actor_loss_weight = actor_loss_weight
      self._critic_loss_weight = critic_loss_weight
      self._alpha_loss_weight = alpha_loss_weight
      self._td_errors_loss_fn = td_errors_loss_fn
      self._gamma = gamma
      self._reward_scale_factor = reward_scale_factor
      self._target_entropy = target_entropy
      self._gradient_clipping = gradient_clipping
      self._debug_summaries = debug_summaries
      self._summarize_grads_and_vars = summarize_grads_and_vars
      self._update_target = self._get_target_updater(
          tau=self._target_update_tau, period=self._target_update_period
      )
  
      train_sequence_length = (
          2 if not critic_network.state_spec else None
      )
  
      super(SacAgent, self).__init__(
          time_step_spec,
          action_spec,
          policy=policy,
          collect_policy=policy,
          train_sequence_length=train_sequence_length,
          debug_summaries=debug_summaries,
          summarize_grads_and_vars=summarize_grads_and_vars,
          train_step_counter=train_step_counter,
      )
  
      self._as_transition = data_converter.AsTransition(
          self.data_context,
          squeeze_time_dim=(train_sequence_length == 2),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Callable, Optional, Text
  import gin
  import numpy
  from six.moves import zip
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import network
  from tf_agents.policies import actor_policy
  from tf_agents.policies import tf_policy
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
  from tf_agents.utils import object_identity
label_desc: Use image node_id 1 to create a new SacAgent object from inherited base classes: tf_agent with example: obj = SacAgent(time_step_spec, action_spec, critic_network, actor_network, actor_optimizer, critic_optimizer, alpha_optimizer, actor_loss_weight, critic_loss_weight, alpha_loss_weight, actor_policy_ctor, critic_network_2, target_critic_network, target_critic_network_2, target_update_tau, target_update_period, td_errors_loss_fn, gamma, reward_scale_factor, initial_log_alpha, use_log_alpha_in_alpha_loss, target_entropy, gradient_clipping, debug_summaries, summarize_grads_and_vars, train_step_counter, name)