MSIA 490-24: Reinforcement Learning for Artificial Intelligence

Quarter Offered

Fall ; Diego Klabjan


This course covers the modeling concepts and the underlying recursive functions behind reinforcement learning. A substantial part of the material is devoted to modern algorithms for solving the underlying model including deep Q-networks, policy gradient, Monte-Carlo tree search, and actor-critic algorithms. Development in python and tensorflow is required.

The main objectives are

  1. Given a business problem identify if reinforcement learning is needed to solve it or a different technique
  2. Recognize states, the role of an agent, its actions, and environment for a given reinforcement learning problem
  3. Select the most appropriate reinforcement learning algorithm for a problem at hand