prediction.td_0_prediction
==========================

.. py:module:: prediction.td_0_prediction


Classes
-------

.. autoapisummary::

   prediction.td_0_prediction.TD0Prediction


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

.. py:class:: TD0Prediction(env: gymnasium.Env, policy: gridmind.policies.base_policy.BasePolicy, step_size: float = 0.1, discount_factor: float = 0.9, summary_dir: Optional[str] = None, write_summary: bool = True)

   Bases: :py:obj:`gridmind.algorithms.base_learning_algorithm.BaseLearningAlgorithm`


   Tabular TD(0) for estimating V_pi.
   Input: policy to be evaluated. The policy is supposed to be a function whose input is observation and output is action.


   .. py:attribute:: step_size
      :value: 0.1



   .. py:attribute:: V


   .. py:attribute:: policy


   .. py:attribute:: discount_factor
      :value: 0.9



   .. py:method:: _get_state_value_fn(force_functional_interface: bool = True)


   .. py:method:: _get_state_action_value_fn(force_functional_interface: bool = True)


   .. py:method:: _get_policy()


   .. py:method:: _train_steps(num_steps: int, prediction_only: bool, *args, **kwargs)
      :abstractmethod:



   .. py:method:: _train_episodes(num_episodes: int, prediction_only: bool = True)


   .. py:method:: set_policy(policy: gridmind.policies.base_policy.BasePolicy, **kwargs)
      :abstractmethod: