UCL / Google DeepMind (David Silver) · on UCL / Google DeepMind
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.
Advanced learners with ML and probability. This is an intermediate course in Reinforcement Learning & Robotics, running about 10 lectures, ~1.5 hours each.
Free. davidsilver.uk/teaching redirects here; videos hosted on YouTube (DeepMind channel).
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.
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.
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.