def sharpened_network_policy(
    temperature,
    temperature_multiplier=1.0,
    **kwargs
):
  """Expert function that runs a policy network with lower temperature.

  Args:
    temperature: Temperature passed from the Agent.
    temperature_multiplier: Multiplier to apply to the temperature to "sharpen"
      the policy distribution. Should be <= 1, but this is not a requirement.
    **kwargs: Keyword arguments passed to network_policy.

  Returns:
    Pair (action, dist_inputs) where action is the action taken and dist_inputs
    is the parameters of the policy distribution, that will later be used for
    training.
  """
  return network_policy(
      temperature=(temperature_multiplier * temperature),
      **kwargs
  )
Expert function that runs a policy network with lower temperature.

  Args:
    temperature: Temperature passed from the Agent.
    temperature_multiplier: Multiplier to apply to the temperature to "sharpen"
      the policy distribution. Should be <= 1, but this is not a requirement.
    **kwargs: Keyword arguments passed to network_policy.

  Returns:
    Pair (action, dist_inputs) where action is the action taken and dist_inputs
    is the parameters of the policy distribution, that will later be used for
    training.
  
trax/rl/training.py