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Reinforcement Learning

IIT Madras, , Prof. Dr. B. Ravindran

Updated On 02 Feb, 19

Overview

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.

Includes

Lecture 1: Tutorial 1 - Probability Basics 1

4.1 ( 11 )

Lecture Details

Course Details

COURSE LAYOUT

Week 1    Introduction
Week 2    Bandit algorithms UCB, PAC
Week 3    Bandit algorithms Median Elimination, Policy Gradient
Week 4    Full RL & MDPs
Week 5    Bellman Optimality
Week 6    Dynamic Programming & TD Methods
Week 7    Eligibility Traces
Week 8    Function Approximation
Week 9    Least Squares Methods
Week 10  Fitted Q, DQN & Policy Gradient for Full RL
Week 11  Hierarchical RL
Week 12  POMDPs

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Comments
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Sam

Excellent course helped me understand topic that i couldn't while attendinfg my college.

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Dembe

Great course. Thank you very much.

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