New: New Lecture (June 30) !
Welcome
Welcome to Spring 2026 offering of Deep Reinforcement Learning course at Sharif University of Technology! We are excited to have you join us on this journey into the world of deep reinforcement learning.
Previous Course (Spring 2025): DeepRLCourse Spring 2025
Learning Objectives
- Understand the fundamentals of reinforcement learning
- Apply reinforcement learning to various domains
- Use deep learning techniques to handle large state spaces in RL
- Master the concepts and gain practical understanding of RL
- Gain hands-on experience with important RL problems
- Equip students with enough theoretical knowledge to understand research papers
Instructor
Dr. Mohammad Hossein Rohban
Instructor
Schedule
Session # |
Topic of the Session |
Date |
Deliverables |
|---|---|---|---|
| Session 1 | Introduction to RL | 3 اسفند (Feb 22) |
- |
| Session 2 | Introduction to RL | 5 اسفند (Feb 24) |
- |
| Session 3 | Value Based | - - |
- |
| Session 4 | Value Based | - - |
- |
| Session 5 | Value Based | - - |
- |
| Session 6 | Value Based | - - |
- |
| Session 7 | Policy Based | - - |
- |
| Session 8 | Policy Based | 6 اردیبهشت (April 26) |
- |
| Session 9 | Policy Based | 8 اردیبهشت (April 28) |
Policy Based TA Session/HW2 Release |
| Session 10 | Actor Critic | 13 اردیبهشت (May 3) |
- |
| Session 11 | Actor Critic | 15 اردیبهشت (May 5) |
Actor Critic TA Session |
| Session 12 | Model Based | 20 اردیبهشت (May 10) |
- |
| Session 13 | Model Based | 22 اردیبهشت (May 12) |
- |
| Session 14 | Multi-Armed Bandit | 27 اردیبهشت (May 17) |
- |
| Session 15 | Boundaries of regret | 29 اردیبهشت (May 19) |
- |
| Session 16 | Exploration in full RL | 3 خرداد (May 24) |
- |
| Session 17 | Exploration in full RL | 5 خرداد (May 26) |
MAB TA Session |
| Session 18 | Imitation Learning | 10 خرداد (May 31) |
- |
| Session 19 | Imitation and Inverse RL | 12 خرداد (June 3) |
- |
| Session 20 | Offline RL | 17 خرداد (June 7) |
- |
| Session 21 | Offline RL | 19 خرداد (June 9) |
Midterm |
| Session 22 | Meta Learning | 24 خرداد (June 14) |
- |
| Session 23 | Meta Learning | 26 خرداد (June 16) |
- |
| Session 24 | Multi Agent RL | 31 خرداد (June 21) |
- |
| Session 25 | Multi Agent RL | 2 تیر (June 23) |
- |
| Session 26 | Multi Agent RL | 7 تیر (June 28) |
- |
| Session 27 | Multi Agent RL | 9 تیر (June 30) |
Final Exam |
Logistics & Policies
-
Lectures: Held on Sundays and Tuesdays from 1:30 PM to 3:00 PM in room 102 of the CE department. (Online here!)
-
TA Sessions: Held on Saturdays from 6:00 PM to 7:30 PM Online here!
Late Submission Policy
- Each student has 14 total late days for the course.
- Up to 3 late days may be used per assignment.
Grading
The grading for the Deep Reinforcement Learning course is structured as follows:
Main Components
- Homeworks: Eight homework assignments, each worth 1 point, plus an additional assignment worth 0.5 points.
- Midterm: An online midterm examination followed by additional oral questioning. The material covered includes all topics from the beginning of the course up to and including Model Based Methods, including the content presented in the TA sessions.
- Final: A comprehensive final examination primarily covering the material taught after the Model Based Methods section. Earlier topics may appear indirectly as prerequisite knowledge, but the exam will directly focus on concepts introduced after that point.
- Project: A mini research project in Deep Reinforcement Learning (DRL). Further details and guidelines regarding the project will be announced soon.
| Component | Points | Date | Details |
|---|---|---|---|
| Homeworks | 8.5 | - | 8 HWs * 1 each + 0.5 |
| Midterm | 5 | 22 خرداد June 12 |
@ 09:00 AM |
| Final | 7 | 28 تیر July 19 |
@ 14:30 PM |
| Project | 2.5 | ؟ (?) |
- |
| Total | 23 | - | - |
Head Assistants
Ali Najar
Head TA
Mohammad Pouya Toroghi
Head TA
Teaching Assistants
-
Parsa Ghezelbash
Teaching Assistant
-
Amir Kooshan Fattah
Teaching Assistant
-
Mohammad Amin Abbasfar
Teaching Assistant
-
Arian Komaei
Teaching Assistant
-
Mazdak Teymourian
Teaching Assistant
-
Ramtin Moslemi
Teaching Assistant
-
Danial Parnian
Teaching Assistant
-
Amirmahdi Meighani
Teaching Assistant
-
Mobin Bagherian
Teaching Assistant
-
Amir Malekhosseini
Teaching Assistant
-
Ali Soltani
Teaching Assistant
-
Mahshid Dehghani
Teaching Assistant
-
Milad Hosseini
Teaching Assistant
-
Alireza Nobakht
Teaching Assistant
-
Amir Homayoun Sharifi-zadeh
Teaching Assistant
-
Saeed Terik
Teaching Assistant
-
Hamidreza Ebrahimpour
Teaching Assistant
-
Arshia Izadyari
Teaching Assistant
-
SeyedAhmad MousaviAwal
Teaching Assistant
Acknowledgements
We would like to express our gratitude to the following individuals for their invaluable contributions to the Spring 2025 and 2024 offerings of this course. Their efforts have been instrumental in the development and success of this course.
This offering and all of these changes are thanks to their effort in starting this course.