Lecture 1: Foundations of Reinforcement Learning

1. Introduction to Reinforcement Learning

• Multi-armed Bandits
• Contextual Bandits

2. Markov Decision Processes (MDPs)

• Time-varying MDPs
• Partially Observable MDPs

3. Existing of the Optimal Stationary Policy