artlib.reinforcement
====================

.. py:module:: artlib.reinforcement

.. autoapi-nested-parse::

   Reinforcement learning (RL) is a type of machine learning where agents learn to make
   decisions by interacting with an environment and receiving feedback in the form of
   rewards or penalties. The SARSA (State-Action-Reward-State-Action) algorithm is an on-
   policy RL method that updates the agent’s policy based on the action actually taken.
   This contrasts with Q-learning, which is off-policy and learns the optimal action
   independently of the agent’s current policy.

   Reactive learning, on the other hand, is a more straightforward approach where decisions
   are made solely based on immediate observations, without the complex state-action-reward
   feedback loop typical of RL models like SARSA or Q-learning. It lacks the depth of
   planning and long-term reward optimization seen in traditional RL.

   The modules herein only provide for reactive and SARSA style learning.

   `SARSA <https://en.wikipedia.org/wiki/Reinforcement_learning#SARSA>`_

   `Reactive agents <https://en.wikipedia.org/wiki/Reactive_planning>`_



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/artlib/reinforcement/FALCON/index