MDPlike

MDPlike is a Python library designed to facilitate the implementation and study of Markov Decision Processes (MDPs). It aims to provide a flexible and user-friendly framework for defining MDPs, experimenting with different reinforcement learning algorithms, and analyzing their performance. The library offers abstractions for core MDP components such as states, actions, transitions, and rewards, allowing users to construct custom MDP environments.

Beyond defining MDPs, MDPlike includes implementations of several foundational reinforcement learning algorithms. These include value iteration, policy iteration, and Q-learning, among others. The library is structured to allow users to easily plug in their custom MDP environments with these algorithms and observe the learning process. It also provides tools for visualizing learning curves and analyzing the resulting policies.

The design philosophy of MDPlike emphasizes clarity and modularity. This enables researchers and students to understand the underlying mechanisms of reinforcement learning algorithms and to extend the library with new algorithms or more complex MDP formulations. It is particularly useful for educational purposes, offering a practical way to grasp theoretical concepts in reinforcement learning. The library is developed in Python, leveraging common scientific computing libraries.