MSIA 490-24: Reinforcement Learning for Artificial Intelligence

Quarter Offered

Fall ; Diego Klabjan


Recent breakthroughs of artificial intelligence in games such as Go and poker, and advancements in robotics, autonomous cars, manufacturing scheduling are driven by reinforcement learning where an agent learns in real time based on an ever-changing environment and receiving a reward after an action is taken. Combined with deep learning, deep reinforcement learning is the current state-of-the-art.

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. Through homework assignments and projects, students implement and evaluate these algorithms in Python.

 Libraries used: either Tensorflow ( in Python, or Ray ( in Python.