This project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings.
The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and mitigating maximization bias, while supporting the exploration of discrete state-space modeling and probabilistic decision-making strategies.
Users can engage with various simulation scenarios, ranging from multi-armed bandit modeling to grid world navigation and game-based tasks like tic-tac-toe. These tools facilitate the study of how agents balance exploration and exploitation to maximize cumulative rewards within structured, discrete environments.
