duo_ai.policies.logit
=====================

.. py:module:: duo_ai.policies.logit


Classes
-------

.. autoapisummary::

   duo_ai.policies.logit.LogitPolicyConfig
   duo_ai.policies.logit.LogitPolicy


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

.. py:class:: LogitPolicyConfig

   Configuration dataclass for LogitPolicy.

   :param name: Name of the policy class. Default is "logit".
   :type name: str, optional
   :param metric: Confidence metric to use. Default is "max_logit".
   :type metric: str, optional
   :param threshold: Confidence threshold for expert query. Default is None.
   :type threshold: float, optional
   :param temperature: Temperature for scaling logits. Default is None.
   :type temperature: float, optional
   :param load_path: Path to a checkpoint to load. Default is None.
   :type load_path: str, optional

   .. rubric:: Examples

   >>> config = LogitPolicyConfig(metric="max_prob", threshold=0.8)


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



   .. py:attribute:: metric
      :type:  str
      :value: 'max_logit'



   .. py:attribute:: threshold
      :type:  Optional[float]
      :value: None



   .. py:attribute:: temperature
      :type:  Optional[float]
      :value: None



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



.. py:class:: LogitPolicy(config: LogitPolicyConfig, env: gym.Env)

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


   Policy that selects actions based on logit confidence metrics and thresholds.

   .. rubric:: Examples

   >>> policy = LogitPolicy(LogitPolicyConfig(), env)
   >>> obs = ...
   >>> action = policy.act(obs)


   .. py:attribute:: config_cls


   .. py:attribute:: config


   .. py:attribute:: params


   .. py:attribute:: device


   .. py:attribute:: EXPERT


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

      Select actions based on confidence scores and threshold.

      :param obs: Observation dictionary containing 'novice_logits'.
      :type obs: dict
      :param temperature: Unused. Included for API compatibility.
      :type temperature: float, optional

      :returns: Tensor of selected actions (expert or not) for the batch.
      :rtype: torch.Tensor

      .. rubric:: Examples

      >>> action = policy.act(obs)



   .. py:method:: compute_confidence(logits: torch.Tensor) -> torch.Tensor

      Compute confidence scores from logits using the configured metric.

      :param logits: Logits tensor from the policy.
      :type logits: torch.Tensor

      :returns: Confidence scores for each sample in the batch.
      :rtype: torch.Tensor

      :raises NotImplementedError: If the configured metric is not recognized.

      .. rubric:: Examples

      >>> score = policy.compute_confidence(logits)



   .. 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: numpy.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

      :raises KeyError: If a parameter key is not recognized by the policy.

      .. rubric:: Examples

      >>> policy.set_params({'threshold': 0.7})



   .. 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()