CSE4/546: Reinforcement Learning

Description

Reinforcement learning is an area of machine learning, where an agent or a system of agents learn to archive a goal by interacting with their environment. RL is often seen as the third area of machine learning, in addition to supervised and unsupervised areas, in which learning of an agent occurs as a result of its own actions and interaction with the environment.

In recent years there has been success in reinforcement learning research in both theoretical and applied fields. It was applied in a variety of fields such as robotics, pattern recognition, personalized medical treatment, drug discovery, speech recognition, computer vision, and natural language processing. This course primarily focuses on training students to frame reinforcement learning problems and to tackle algorithms from dynamic programming, Monte Carlo and temporal-difference learning. Students will progress towards larger state space environments using function approximation, deep Q-networks and state-of-the-art policy gradient algorithms. We will also go over the recent methods that are based on reinforcement learning, such as imitation learning, meta learning and more complex environment formulations.