text: # coding=utf-8
  # Copyright 2018 The TF-Agents Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Policy implementation that applies temperature to a distribution."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import gin
  import tensorflow as tf  # pylint: disable=g-explicit-tensorflow-version-import
  
  from tf_agents.policies import tf_policy
  
  
  @gin.configurable
  class BoltzmannPolicy(tf_policy.TFPolicy):
    """Returns boltzmann samples of a given policy.
  
    The wrapped policy must expose a distribution parameterized by logits.
    """
  
    def __init__(self, policy, temperature=1.0, name=None):
      """Builds a BoltzmannPolicy wrapping the given policy.
  
      Args:
        policy: A policy implementing the tf_policy.TFPolicy interface, using
          a distribution parameterized by logits.
        temperature: Tensor or function that returns the temperature for sampling
          when `action` is called. This parameter applies when the action spec is
          discrete. If the temperature is close to 0.0 this is equivalent to
          calling `tf.argmax` on the output of the network.
        name: The name of this policy. All variables in this module will fall
          under that name. Defaults to the class name.
      """
      super(BoltzmannPolicy, self).__init__(
          policy.time_step_spec,
          policy.action_spec,
          policy.policy_state_spec,
          policy.info_spec,
          emit_log_probability=policy.emit_log_probability,
          clip=False,
          name=name)
      self._temperature = temperature
      self._wrapped_policy = policy
  
    def _variables(self):
      return self._wrapped_policy.variables()
  
    def _get_temperature_value(self):
      if callable(self._temperature):
        return self._temperature()
      return self._temperature
  
    def _apply_temperature(self, dist):
      """Change the action distribution to incorporate the temperature."""
      logits = dist.logits / self._get_temperature_value()
      return dist.copy(logits=logits)
  
    def _distribution(self, time_step, policy_state):
      distribution_step = self._wrapped_policy.distribution(
          time_step, policy_state)
      if self._temperature is None:
        return distribution_step
  
      action_dist = tf.nest.map_structure(self._apply_temperature,
                                          distribution_step.action)
      return distribution_step._replace(action=action_dist)
  