duo_ai.algorithms.random
========================

.. py:module:: duo_ai.algorithms.random


Classes
-------

.. autoapisummary::

   duo_ai.algorithms.random.RandomAlgorithmConfig
   duo_ai.algorithms.random.RandomAlgorithm


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

.. py:class:: RandomAlgorithmConfig

   Configuration dataclass for RandomAlgorithm.

   :param name: Name of the algorithm class. Default is "random".
   :type name: str, optional
   :param probs: List of probabilities to search. Default is np.arange(0, 1.01, 0.1).
   :type probs: list of float, optional

   .. rubric:: Examples

   >>> config = RandomAlgorithmConfig()


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



   .. py:attribute:: probs
      :type:  List[float]


.. py:class:: RandomAlgorithm(config: RandomAlgorithmConfig)

   Bases: :py:obj:`duo_ai.core.Algorithm`


   Algorithm that searches for the best probability parameter to maximize evaluation reward.

   .. rubric:: Examples

   >>> algo = RandomAlgorithm(RandomAlgorithmConfig())


   .. py:attribute:: config_cls


   .. py:attribute:: config


   .. py:method:: train(policy: duo.policies.PPOPolicy, env: gym.Env, validators: Dict[str, duo.core.Evaluator]) -> None

      Train the RandomAlgorithm by searching for the best probability parameter that maximizes evaluation reward.

      :param policy: The policy to be evaluated and tuned.
      :type policy: duo.policies.PPOPolicy
      :param env: The environment instance for training and data generation.
      :type env: gym.Env
      :param validators: Dictionary mapping split names to evaluator instances for evaluation.
      :type validators: dict of str to duo.core.Evaluator

      :rtype: None

      .. rubric:: Examples

      >>> algorithm = RandomAlgorithm(RandomAlgorithmConfig())
      >>> algorithm.train(policy, env, validators)



   .. py:method:: save_checkpoint(policy: duo.policies.PPOPolicy, name: str) -> None

      Save the current policy configuration and parameters to a checkpoint file.

      :param policy: The policy whose parameters are to be saved.
      :type policy: duo.policies.PPOPolicy
      :param name: Name for the checkpoint file.
      :type name: str

      :rtype: None

      .. rubric:: Examples

      >>> self.save_checkpoint(policy, "best_test")