Asst. Prof. Dr.-Ing. Mahdi Bohlouli

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Reinforcement Learning Course



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 maximising 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 since some years, has been highlighted and gained again the attention of ML researchers in the recent years, specially 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
Week 1 26.04.2020 Lecture Bohlouli Introduction to the course and logistics Course_Intro.pdf
28.04.2020 Lecture Bohlouli An Introduction to Reinforcement Learning RL_Intro.pdf
Week 2 03.05.2020 Tutorial Bohlouli Markov Decision Process MDP.pdf
05.05.2020 Tutorial Nazeri Elements of RL Systems: Environments Blackboard
Week 3 11.05.2020 Lecture Bohlouli Planning by Dynamic Programming Planning_DP.pdf
12.05.2020 Lecture Bohlouli Components of RL Agent RL_Comp.pdf
Week 4 17.05.2020 Lecture Bohlouli
19.05.2020 Tutorial Nazeri Elements of RL Systems: Action Values Blackboard
Week 5 24.05.2020 Lecture Bohlouli Public Holiday
26.05.2020 Lecture Bohlouli
Week 6 30.05.2020 Lecture Bohlouli
02.06.2020 Tutorial Nazeri
Week 7 10.06.2020 Lecture Bohlouli
11.06.2020 Lecture Bohlouli
Week 8 17.06.2020 Lecture Bohlouli
19.06.2020 Tutorial Nazeri
Week 9 24.06.2020 Lecture Bohlouli
26.06.2020 Lecture Bohlouli
Week 10 05.07.2020 Lecture Bohlouli
07.07.2020 Tutorial Nazeri


Assignment # Release Date Description Submission Deadline Source Files
Assignment 1 05.05.2020 Soccer environment 19.05.2020, 16:59 CEST
Assignment 2 19.05.2020 Action Values 01.06.2020, 23:59 CEST Assignment 2, Supp_materials
Assignment 3 02.06.2020 15.06.2020, 23:59 CEST
Assignment 4 16.06.2020 29.06.2020, 23:59 CEST
Assignment 5 30.07.2020 13.07.2020, 23:59 CEST

Course Student Presentations:

Date Topic Student Name Slides
12.05.2020 Policy Gradient Methods for Reinforcement Learning with Function Approximation
19.05.2020 A Natural Policy Gradient Amirreza Mohammadi
26.05.2020 Approximately Optimal Approximate Reinforcement Learning Ehsan Rassekh
02.06.2020 An Analysis of Temporal-Difference Learning with Function Approximation
09.06.2020 Reinforcement Learning of Motor Skills with Policy Gradients
16.06.2020 Algorithms for inverse reinforcement learning
23.06.2020 Robot learning from demonstrations Sarina Danaei
30.06.2020 The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems Fatemeh Merhara
07.07.2020 Hysteretic Q-Learning : an algorithm for decentralized reinforcement learning in cooperative multi-agent teams Reza Khaleghi
14.07.2020 Kernel-Based Reinforcement Learning


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 a Unix-style operating systems


Class Time and Location:

  • Sundays, 14:30 – 16:00 CET.
  • Tuesdays, 14:30 – 16:00 CET.
  • 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: [–xea-944]

Final Exam:

  • Final exam will be in the written form.
  • The final exam will be held on 27.06.2020, at 06:30 CET.

Course Links:

Piazza Course Page: []
Course Videos Link: [–xea-944]


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