CS980: Advanced ML: Reinforcement Learning

Please see the class homepage for more information.

Where and When

  • Kingsbury N233
  • TR 3:40pm - 5:00pm


This seminar will cover reinforcement learning. The goal in reinforcement learning is to learn how to act while interacting with a dynamic and complex environment. Instead of trying to cover all reinforcement learning topics, we will focus on the foundations needed to understand other concepts. Below are some examples of relevant topics:

  1. Markov decision processes: Policy iteration, Value iteration, Linear programming
  2. Standard RL methods: SARSA, TD, Q-learning
  3. Model-based methods: Dyna and others
  4. Value Function Approximation: LSTD, LSPI, ALP, ABP
  5. Uncertainty: Exploration and Robustness

We will use Python, R, or C++ and cover relevant topics from linear algebra, mathematical optimization, and statistics as needed.


Richard Sutton, Andrew Barto, Reinforcement Learning: An Introduction, 2018.