Chapter 1: Introduction to Reinforcement Learning
Chapter 2: The Foundation - Markov Decision Processes
Chapter 3: Model Based Approaches
Chapter 4: Model Free Approaches
Chapter 5: Function Approximation and Deep Reinforcement Learning
Chapter 6: Deep Q-Learning (DQN)
Chapter 7: Improvements to DQN
Chapter 8: Policy Gradient Algorithms
Chapter 9: Combining Policy Gradient and Q-Learning
Chapter 10: Integrated Planning and Learning
Chapter 11: Proximal Policy Optimization (PPO) and RLHF
Chapter 12: Introduction to Multi Agent RL (MARL)
Chapter 13: Additional Topics and Recent Advances.