- Example: 4x3 Grid World: Markov Decision Processes solved with Value Iteration and Policy Iteration (in R)
- Example: L-Maze: Solving a Maze using Value Iteration (in R with package markovDP)
- Connection to playing games (Chapter 5): Finding the Optimal Policy to Play Tic-Tac-Toe with Value Iteration implements value iteration to find the optimal policy to play the game. (Python)
- Example: 4x3 Grid World: A Q-Learning Agent (in R)
- Example: L-Maze: Solving a Maze using Q-Learning (in R with package markovDP)
- Connection to playing games (Chapter 5): Learning to Play Tic-Tac-Toe with Q-Learning implements a simple table-based Q-learning algorithm to play the game. (Python)
These examples implement methods described in the book Reinforcement Learning: An Introduction by Sutton and Barto (2020).
- Example: 4x3 Grid World: Monte Carlo Control (in R)
- Example: 4x3 Grid World: TD Control with Sarsa, Q-Learning and Expected Sarsa (in R)
- R package: markovDP
- Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms.
- CleanRL is a Deep Reinforcement Learning library.
© 2025 Michael Hahsler. All code and documents in this repository are provided under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License.
