Reinforcement Learning Algorithms from Sutton's Book

This GitHub repository provides code implementing algorithms and models from Sutton's renowned reinforcement learning textbook, "Reinforcement Learning: An Introduction." The code covers various model-free solvers, requiring only the definition of states, actions, and a transition function. Examples include a single-state infinite variance problem and a Monte Carlo Tree Search maze solver. While not optimized for production, it's a valuable resource for learning reinforcement learning and implementing algorithms from scratch.