CS980: Advanced ML: Reinforcement Learning

Please see the class homepage for schedule and other up-to-date information.

Where and When

  • Kingsbury N233
  • MW 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.

  1. Markov decision processes: Policy iteration, Value iteration, Linear programming
  2. Value Function Approximation: LSTD, LSPI, ALP, ABP
  3. Uncertainty: Bandits and Robust Markov Decision Processes

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

More Info

Please see the class homepage.