duo_ai.policies.always
======================

.. py:module:: duo_ai.policies.always


Classes
-------

.. autoapisummary::

   duo_ai.policies.always.AlwaysPolicyConfig
   duo_ai.policies.always.AlwaysPolicy


Module Contents
---------------

.. py:class:: AlwaysPolicyConfig

   Configuration dataclass for AlwaysPolicy.

   :param name: Name of the policy class. Default is "always".
   :type name: str, optional
   :param agent: The agent type to always select. Options are "novice" or "expert". Default is "novice".
   :type agent: str, optional
   :param load_path: Path to a checkpoint to load. Default is None.
   :type load_path: str, optional

   .. rubric:: Examples

   >>> config = AlwaysPolicyConfig(agent="expert")


   .. py:attribute:: name
      :type:  str
      :value: 'always'



   .. py:attribute:: agent
      :type:  str
      :value: 'novice'



   .. py:attribute:: load_path
      :type:  Optional[str]
      :value: None



.. py:class:: AlwaysPolicy(config: AlwaysPolicyConfig, env: gym.Env)

   Bases: :py:obj:`duo_ai.core.policy.Policy`


   Policy that always selects the same agent (novice or expert) for every action.

   .. rubric:: Examples

   >>> policy = AlwaysPolicy(AlwaysPolicyConfig(agent="novice"), env)
   >>> obs = ...
   >>> action = policy.act(obs)


   .. py:attribute:: config_cls


   .. py:attribute:: choice


   .. py:attribute:: device


   .. py:attribute:: config


   .. py:method:: act(obs: Any, temperature: Optional[float] = None) -> torch.Tensor

      Select the constant action for a batch of observations.

      :param obs: Batch of observations. If dict, must contain 'base_obs'.
      :type obs: dict or np.ndarray
      :param temperature: Unused. Included for API compatibility.
      :type temperature: float, optional

      :returns: Tensor of constant actions (agent indices) for the batch.
      :rtype: torch.Tensor

      :raises ValueError: If obs is not a dict or numpy array.

      .. rubric:: Examples

      >>> action = policy.act(obs)



   .. py:method:: reset(done: numpy.ndarray) -> None

      Reset the policy state at episode boundaries.

      :param done: Boolean array indicating which episodes in a batch require a reset.
      :type done: np.ndarray

      :rtype: None

      .. rubric:: Examples

      >>> policy.reset(done)



   .. py:method:: get_params() -> Dict[str, Any]

      Get the current parameters of the policy.

      :returns: Dictionary of policy parameters.
      :rtype: dict

      .. rubric:: Examples

      >>> params = policy.get_params()



   .. py:method:: set_params(params: Dict[str, Any]) -> None

      Set the parameters of the policy.

      :param params: Dictionary of policy parameters to set.
      :type params: dict

      :rtype: None

      .. rubric:: Examples

      >>> policy.set_params(params)



   .. py:method:: train() -> None

      Set the policy to training mode.

      :rtype: None

      .. rubric:: Examples

      >>> policy.train()



   .. py:method:: eval() -> None

      Set the policy to evaluation mode.

      :rtype: None

      .. rubric:: Examples

      >>> policy.eval()