Reinforcement Learning: Chapter 3 Finite Markov Decision Processes

Chapter 3 introduces the mathematical formalism for defining the full reinforcement learning problem in the book. We will cover the definition of probability transition functions, reward signals, and the discounted return. If there is time we will continue with the discussion of policies and value functions as explained with the gridworld example.

As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course