Recommended Resources for Learning RL
joemeno · x · 2026-07-15
This reshare recommends a set of beginner resources for Reinforcement Learning, arguing they are more accessible for newcomers than just staring at formulas.
The original post highlights The Little Book of Reinforcement Learning, which is concise but covers foundational RL, Monte Carlo, DQN, PPO, and more, complete with PyTorch implementations and supplementary derivations on GitHub. The author stresses that in the era of Agents, RLHF, test-time scaling, and self-improving systems, understanding "how models improve behavior through feedback" is increasingly vital.
The user sharing the post adds that when they first learned RL, they read Shiyu Zhao's Mathematical Foundations of Reinforcement Learning, which they also consider an excellent introductory tutorial.
Related event: The Little Book of Reinforcement Learning Released for Beginners(3 posts)→
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