Udacity
Build DQN, policy-gradient, and multi-agent agents in Unity ML-Agents environments across three reviewed projects. Choose it for structured, project-graded practice in deep RL with mentor feedback.
Advanced learners with ML and probability. This is an advanced course in Reinforcement Learning & Robotics, running about ~83 hours (about 4 months part-time).
$249/mo subscription (4-month bundle $212/mo). Udacity All Access subscription pricing seen 2026-11-01; estimated 83 hours of content.
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.
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.