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Courses » Reinforcement Learning

Reinforcement Learning

ABOUT THE COURSE

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.


INTENDED AUDIENCE

All

INDUSTRY SUPPORT – LIST OF COMPANIES/INDUSTRY THAT WILL RECOGNIZE/VALUE THIS ONLINE COURSE

Data analytics/data science/robotics

COURSE INSTRUCTOR



Prof. Ravindran is currently an associate professor in Computer Science at IIT Madras. He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

MORE DETAILS ABOUT THE COURSE


Course url: https://onlinecourses.nptel.ac.in/noc16_cs09
Course duration : 12 weeks
Start date and end date of course: 18 July 2016 - 7 October 2016
Dates of exams :
 
16 October 2016 & 23 October 2016
Time of exam : 2pm - 5pm
Final List of exam cities will be available in exam registration form.
Exam registration url - Will be announced shortly
Exam Fee:
The online registration form has to be filled and the certification exam fee of approximately Rs 1000(non-Programming)/1250(Programming) needs to be paid.

CERTIFICATE

E-Certificate will be given to those who register and write the exam. Certificate will have your name, photograph and the score in the final exam. It will have the logos of NPTEL and IIT Madras.
It will be e-verifiable at nptel.ac.in/noc.

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

REFERENCE BOOKS

R. S. Sutton and A. G. Barto. Reinforcement Learning - An Introduction. MIT Press. 1998.