Learning objectives Get a quick roadmap: what to do first and how to use the course resources. Know where to find detailed advice (long version and FAQ). How to succeed in this course (short version) Follow the order. Use the Course outline and Learning path. Start with prerequisites and math if you need them; then Volume 1 (foundations, bandits, MDPs, DP), then Volume 2 (MC, TD, SARSA, Q-learning), then Volume 3 and beyond. Do not skip the foundations. ...
Learning objectives Know whether this course is aimed at beginners or experts (and how it serves both). Understand the balance between academic theory and practical implementation. Choose a pace that fits your background and goals. Beginners or experts? Beginners (to RL or programming): The course is designed to be accessible. Start with the Learning path Phase 0 if you have no programming. Use Math for RL and Prerequisites before or alongside Volume 1. The Preliminary assessment helps you check readiness. Exercises have hints and worked solutions so you can learn step by step. You do not need a PhD or prior ML course—but you do need to invest time in prerequisites if your background is light. ...
Learning objectives Understand how this curriculum aligns with (and extends beyond) the classic Reinforcement Learning: An Introduction (Sutton & Barto). Know when to use the course vs. the book for depth and exercises. This course vs. the RL book Sutton & Barto (Reinforcement Learning: An Introduction, 2nd ed.) is the standard textbook for RL. It builds from bandits and MDPs through dynamic programming, Monte Carlo, temporal difference, and function approximation, with clear math and many examples (gridworld, blackjack, etc.). This curriculum follows a similar progression for the core topics (bandits → MDPs → DP → MC → TD → approximation) so that if you read the book alongside the course, the order matches. ...