Degree Level Course

Reinforcement Learning

To enable the student to understand the reinforcement learning paradigm, to be able to identify when an RL formulation is appropriate, to understand the basic solution approaches in RL, to implement and evaluate various RL algorithms.

Taught by Prof. Balaraman Ravindran

Code BSDA5007

Credits 4 Credits

Type Elective

Prerequisites None

Co-requisites

BSCS3004

12-Week Roadmap

Course Structure & Syllabus

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For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1

Review of ML fundamentals – Classification, Regression. Review of probability theory and optimization concepts.

WEEK 2

RL Framework; Supervised learning vs. RL; Explore-Exploit Dilemma; Examples.

WEEK 3

MAB: Definition, Uses, Algorithms, Contextual Bandits, Transition to full RL, Intro to full RL problem

WEEK 4

Intro to MDPs: Definitions , Returns, Value function, Q-function.

Faculty & Experts

About the Instructors

Prof. Balaraman Ravindran

Professor , CSE , IIT Madras

B. Ravindran heads the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) at IIT Madras. He is the Mindtree Faculty Fellow, TCS Affiliate Faculty and Professor in the Department of Computer Science and Engineering at IIT Madras. He has held visiting positions at the Indian Institute of Science, University of Technology, Sydney, and Google Research. Currently, his research interests span the areas of geometric deep learning and reinforcement learning. He is one of the founding executive committee members of the India chapter of ACM SIGKDD. He is currently serving on the editorial boards of Machine Learning Journal, JAIR, ACM Transactions on Intelligent Systems and Technology, PLOS One, and Frontiers in Big Data and AI. He has published more than 100 papers in premier journals and conferences. His work with students have won multiple best paper awards, the most recent being the best application paper at PAKDD 2021. His video lectures on NPTEL are widely viewed and have received accolades for their depth and delivery. He received his PhD from the University of Massachusetts, Amherst and his Master’s degree from the Indian Institute of Science, Bangalore. He is a senior member of the Association for Advancement of AI (AAAI) and an ACM Distinguished Member.

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