Asst. Prof. Dr.-Ing. Mahdi Bohlouli

Personal Academic Page

Reinforcement Learning, 2021

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
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10

Assignments:

Assignment # Release Date Description Submission Deadline Source Files
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Assignment 7
Assignment 8
Assignment 9
Assignment 10

Final Project:

Title Release Date Description Submission Deadline Source Files

Prerequisites:

Before commencing this course, you should:
  • 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:

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:

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