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Reinforcement Learning

Reinforcement Learning. Instructor: Prof. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras. Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI. The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research. (from nptel.ac.in)

In this lesson, we provide a brief introduction to contextual bandit problems which are a class of bandit problems in which additional information in the form of context is presented to us at each step before we make an action selection. Important in their own right, they also serve to bridge the gap between the immediate RL problem we have been discussing in the past few lessons and the full RL problem which we will take up next.

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