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)
|Lecture 08 - Bandit Optimalities|
In this lecture we discuss the different solution approaches that are considered for solving bandit problems. These include asymptotic correctness, regret optimality and probably approximately correct (PAC) optimality. These approaches are helpful in applying bandit algorithms to different practical problems.
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