01. Course Overview and Reinforcement Learning Introduction
02. Multi-Armed Bandits
03. Markov Decision Processes
04. Dynamic Programming for Solving MDPs
05. Monte Carlo Methods
06. Temporal Difference Learning I
07. Temporal Difference Learning II
08. Planning and Learning I
09. Planning and Learning II
10. Prediction with Approximation
11. Control with Approximation
12. Off-policy Methods with Approximation
13. Policy Gradient Methods
14. Recent Advances in Deep Reinforcement Learning
15. Final Project Presentations