Reinforcement Learning Course

Overview

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Embark on a comprehensive 17-hour journey into the world of Reinforcement Learning. Explore key concepts from introduction to advanced topics, covering exploration and exploitation, Markov Decision Processes, dynamic programming, model-free prediction and control, function approximation, deep reinforcement learning, policy gradients, actor critics, planning, and models. Gain practical insights through a case study on classic games and take a tour of deep RL agents. Master the fundamentals and cutting-edge techniques of this powerful machine learning paradigm.

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.