Course Description:
Reinforcement Learning (RL) is a Machine Learning (ML) paradigm that focuses on goal-directed learning from interactions. In particular, it is learning how to map situations to actions by maximizing a scalar reward signal. RL is studied in other disciplines such as game theory, robotics, operations research, and multi-agent systems. It has roots in psychology and the advancements in psychology contributed to the advancements of RL and vise versa. It has been existing for some years, has been highlighted, and gained again the attention of ML researchers in recent years, especially in terms of Deep Reinforcement Learning (DRL). In this course, we will cover the difference between RL and other ML paradigms such as supervised learning and unsupervised learning, exploration and exploitation dilemma, main elements of RL systems, model-free, and model-based methods, planning, control and how to design RL algorithms to RL problems.
Learning Outcomes:
At the end of the course you will:
- know the fundamentals of reinforcement learning
- know different RL problems and solutions
- implement RL methods
Course Outline:
| Week # | When | What | Who | Topic | Slides | Recordings |
| Week 1 | 04.04.21 | Lecture | Bohlouli | Course Logistics | intro.pdf | |
| 06.04.21 | Lecture | Bohlouli | Introduction to RL | |||
| Week 2 | 11.04.21 | |||||
| 13.04.21 | ||||||
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Assignments:
| Assignment # | Release Date | Description | Submission Deadline | Source Files |
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Final Project:
| Title | Release Date | Description | Submission Deadline | Source Files |
Prerequisites:
- have experiences and good knowledge of machine learning
- be familiar with linear Algebra
- have solid programming skills in Python
- be familiar with working on Unix-style operating systems
References:
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Reinforcement Learning (Second Edition), by Richard S. Sutton and Andrew G. Barto, Stanford University, 2018.
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Deep Reinforcement Learning Hands-On (2nd Edition), by Maxim Lapan, Packt Publishing; 2 edition (31 Jan 2020).
Class Time and Location:
- Sundays, 10:00 – 11:30 CEST.
- Tuesdays, 10:00 – 11:30 CEST.
- Given the current Corona Situation, this semester, the class will be completely online.
- You should first apply for approval through the following link. This link will be also the online course sessions every week.
- Course Videos Link: [https://elearn.iasbs.ac.ir/b/dr–fyf-d8v-ck5]
Final Exam:
- The final exam will be held on ….
Course Links:
Piazza Course Page: [piazza.com/iasbs.ac.ir/spring2021/rl02]
Course Videos Link: [https://elearn.iasbs.ac.ir/b/dr–fyf-d8v-ck5]
Instructors:
