Deep Reinforcement Learning

Catalog description

Credits 3. 3 Lecture Hours. Fundamentals of formalizing reinforce learning (RL) problems such as Markov Decision Process as well as various techniques and approaches for optimizing the agent's behavior given such problems; exploration of basic concepts and approaches in deep neural networks; overview of state-of-the-art algorithms that integrate deep learning with reinforcement learning; basics of reinforcement learning as well as deep reinforcement learning. Prerequisites: Graduate classification.