Learn reinforcement learning through interactive content. AI-generated, human-reviewed, community-refined.
Different goals, different content. Choose your path based on what you want to accomplish.
Learn the concepts
Progressive lessons from bandits to deep RL. Intuition, math, and code at your pace.
Deep dive research
Understand seminal papers like DQN, PPO, and AlphaGo. Context, insights, and critical analysis.
Solve real problems
Formulate robotics, trading, and recommendation problems as RL. End-to-end guides.
Scale and deploy
Distributed training, experiment tracking, production code, and deployment. Engineering guides.
Experiment hands-on
Interactive playgrounds to test algorithms. GridWorld, bandits, and more in your browser.
Contribute and connect
Report issues, improve prompts, build demos, review content. Open source collaboration.
Content is generated from carefully designed prompts, then reviewed and refined through community feedback.
Structure, examples, and learning objectives defined upfront
Content follows style guides and mathematical conventions
Accuracy, clarity, and pedagogical quality verified
Readers suggest improvements; content and prompts evolve
This is an open project. The content, prompts, and code are all on GitHub. Contributions of all kinds are welcome.
Typos, bugs, unclear explanations
Better prompts mean better content
Interactive visualizations and playgrounds
Help verify accuracy and quality
Created by Enes Bilgin,
author of "Mastering Reinforcement Learning with Python".
An experiment in AI-assisted education—creating high-quality learning resources through human-AI collaboration.