/RL

Introduction to Reinforcement Learning in Python

Primary LanguagePython

Reinforcement Learning

Repository dedicated to the implementation of several Reinforcement Learning methods in Python, exemplified by OpenAI environments. Some performance comparisons are included.

Currently implemented:

  • agentTabular.py: Tabular action value methods.

    • SARSA
    • SARSA(λ)
    • Q-learning
    • Watkins Q-learning

  • agentIncrementalVFA.py: Incremental methods using value function approximation.

    • TD
    • TD(λ)
    • Gradient TD2
    • Gradient Q-learning
    • Recursive Least Squares TD

  • agentBatchVFA.py: Batch methods using value function approximation.

    • Least Squares TD
    • Least Squares TD(λ)
    • Least Squares TDQ
    • Least Squares Policy Iteration TD

  • agentActorCritic.py: Actor-critic methods.

    • Q Actor-Critic
    • Advantage Actor-Critic
    • TD Actor-Critic
    • TD(λ) Actor-Critic

  • agentModelBased.py: Model based methods.

    • DynaQ
    • Monte Carlo Tree Search
    • TD Tree Search
    • Dyna2

Several classes and functions required for the above files are contained in util.py.

The environment file gridworld.py might be required for some examples and comparisons.