AIL 722/7022: Reinforcement Learning (Fall 2025)

In this course, we will develop the knowledge and skills to understand, implement and apply recent deep reinforcement learning techniques. This course is intended for graduate and advanced undergraduate students.

Specifically, we set the following course objectives:

1. Formulate sequential decision making problems as Markov Decision Processes and map them to suitable RL algorithms.
2. Use deep RL algorithms on standard benchmark environments.
3. Deploy RL algorithms in various domains in conjunction with other ML techniques.

Prerequisites:

1. Theory: Probability and statistics; Algorithms; Exposure to AI and/or ML will be helpful
2. Implementation: Fluency in a high level programming language; Familiarity with installing and working with Github packages; Experience with using high-performance compute services will be helpful

• Slot: AC
• Class Timings: Tue/Fri 2-3:20 pm
• Venue: LH313.5


• Office hours
• Timing: By appointment
• Venue: 513-D, Academic Complex East

We will use piazza as the forum for students to ask questions about both the course material as well as logistics.

Joining Link

• Instructor: Raunak Bhattacharyya, Assistant Professor, Yardi School of Artificial Intelligence
• Graduate student instructors: Mohit Chhabra, Mahesh Keswani, Hasan Mustafa

Below is a tentative list of topics:

1. Markov Decision Processes
2. Dynamic Programming; Approximations
3. Monte Carlo Prediction and Control
4. Temporal-Difference Methods
5. Exploration-Exploitation Tradeoff
6. Policy Gradients
7. Imitation learning
8. Offline RL

Textbooks

1. Introduction to Reinforcement Learning, Sutton and Barto
2. Algorithms for Optimisation, Kochenderfer and Wheeler
3. Algorithms for Decision Making, Kochenderfer, Wheeler and Wray
4. Planning with Markov Decision Processes, Mausam and Kolobov

The weekly schedule will be updated with lecture slides and pointers to additional references as we progress through the course.

Grading policy (tentative, upto ±15% on each component):

1. Programming Assignments: 60%
2. Exams: 40%
3. Audit policy: Only based on exams: To get an audit pass, you should obtain at least 50% absolute score on each and every exam.