Reinforcement Learning Course

This course surveys reinforcement learning from introductory concepts through exploration, Markov decision processes, model-free prediction and control, function approximation, deep reinforcement learning, policy gradients, actor-critic methods, planning, and advanced topics. It concludes with a tour of deep RL agents and a classic games case study.

Syllabus

Reinforcement Learning 1: Introduction to Reinforcement Learning.
Reinforcement Learning 2: Exploration and Exploitation.
Reinforcement Learning 3: Markov Decision Processes and Dynamic Programming.
Reinforcement Learning 4: Model-Free Prediction and Control.
Reinforcement Learning 5: Function Approximation and Deep Reinforcement Learning.
Reinforcement Learning 6: Policy Gradients and Actor Critics.
Reinforcement Learning 7: Planning and Models.
Reinforcement Learning 8: Advanced Topics in Deep RL.
Reinforcement Learning 9: A Brief Tour of Deep RL Agents.
Reinforcement Learning 10: Classic Games Case Study.