OpenAI · on OpenAI Academy
OpenAI's educational package pairs a concise introduction to RL theory with clean reference implementations of VPG, TRPO, PPO, DDPG, TD3, and SAC, plus a curated key-papers list and exercises. Use it to bridge the gap between lecture understanding and writing your own deep RL code.
Advanced learners with ML and probability. This is an intermediate course in Reinforcement Learning & Robotics, running about Self-paced (docs, exercises, and code).
Free. No payment required at any point.
Train agents from Q-learning to PPO and multi-agent setups in Colab, publish them to the Hub, and compete on leaderboards. Ideal for learners who want deep RL to be practical and fun from the first unit, with a free certificate at the end.
David Silver's classic lectures cover MDPs, dynamic programming, Monte Carlo and TD learning, function approximation, policy gradients, and planning, closely tracking the Sutton and Barto textbook. Still the standard first RL course for anyone serious about the field.
Taught by Martha and Adam White with guest appearances from Rich Sutton, this specialization implements the algorithms of the Sutton and Barto textbook from bandits to a complete capstone agent. The most rigorous MOOC path through RL fundamentals.