University of Alberta / Alberta Machine Intelligence Institute · on Coursera
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.
Advanced learners with ML and probability. This is an intermediate course in Reinforcement Learning & Robotics, running about 2 months at 10 hrs/week (4 courses).
Free to audit · $49/mo Coursera subscription for certificate. Included with Coursera Plus; financial aid available. FAQ recommends 4-6 months. Seen 2026-11-01.
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.
Emma Brunskill's course covers tabular RL, deep Q-learning, policy gradients, exploration, offline RL, and RLHF with public lecture slides and assignments. A strong theory-plus-practice sequel to David Silver's lectures.