Part 1 - Elementary Reinforcement Learning
1 - Introduction to Reinforcement Learning
2 - Markov Decision Processes: Markov Process
2 - Markov Decision Processes: Markov Decision Process
3 - Planning by Dynamic Programming: Policies
3 - Planning by Dynamic Programming: Values and DP
4 - Model-Free Prediction
5 - Model-Free Control
Part 2 - Reinforcement Learning in Practice
6 - Value Function Approximation
7 - Policy Gradient Methods
8 - Integrating Learning and Planning
9 - Exploration and Exploitation
10 - Case Study: RL in Classic Games