INTRODUCTION TO REINFORCEMENT LEARNING

Catalog description

Introduce key concepts in intelligent decision-making, specifically reinforcement learning. Topics include agent representation, Markov decision process, fundamentals of model-free and model-based decision-making under uncertainty, value iteration, policy iteration, temporal difference methods, actor-critic methods, and imitation learning.