Deep Reinforcement Learning (CS698R) : Fall 2021

In this course we will explore how an agent (via interactions with the environment) can learn by trial and error. This is quite different from supervised machine learning and comes close to how humans learn by interactions. Reinforcement Learning (RL) deals with problems that require sequential decision making. This course will explore foundations of reinforcement learning. We will study different algorithms for RL and later in the course we will explore how functional approximation in RL algorithms could be done using neural networks giving rise to deep reinforcement learning. We will focus mainly on Value-based methods, Policy-based methods and combination of the two.

CS698R is a research project based course, participants are required to work on open and unsolved research problems in RL and consequently considerable effort is expected from the participant.

All the course contents (lectures, videos, assignments, etc.) are available on HelloIITK Platform.