I will offer an introductory exploration into the field of Reinforcement Learning (RL) with a focus on Markov Decision Processes (MDPs). The first session provides a foundational understanding of RL, covering key concepts such as agents, environments, rewards, and actions. It explains the RL problem framework and introduces MDPs, exploring their role as the mathematical framework underpinning RL.

The second session delves into core algorithms, including Q-learning and policy gradients. The lecture highlights the connection between MDPs and dynamic programming techniques, emphasizing policy iteration and value iteration. Time allowing, I will finalize with a brief description of some recent research topics and results.

A good introduction to RL is the 2018 book on the subject by Sutton and Barto. We will talk about topics in Chapters 1,2-6 and 13.

A more rigorous introduction to MDPs, including convergence results, can be found in the book by Puterman:

Martin L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005.

I will offer an introductory exploration into the field of Reinforcement Learning (RL) with a focus on Markov Decision Processes (MDPs). The first session provides a foundational understanding of RL, covering key concepts such as agents, environments, rewards, and actions. It explains the RL problem framework and introduces MDPs, exploring their role as the mathematical framework underpinning RL.

The second session delves into core algorithms, including Q-learning and policy gradients. The lecture highlights the connection between MDPs and dynamic programming techniques, emphasizing policy iteration and value iteration. Time allowing, I will finalize with a brief description of some recent research topics and results.