Reinforcement Learning

Catalog description

Credits 3. 3 Lecture Hours. Introduction to the theory and practice of reinforcement learning; including Markov decision processes, dynamic programming, Q-Learning, policy gradient algorithms, neural networks, deep reinforcement learning, imitation learning and multi-agent learning. Prerequisite: Graduate classification.