This class will be given during the second semester on Wednesday afternoon. It starts at 1:45pm. The first class takes place on the 3rd of February. Given the COVID-19 crisis, the class will be given on-line using Webex. Access the class by clicking on the following link: https://uliege.webex.com/meet/dernst

The teaching assistants for the class are Samy Aittahar and Bardhyl Miftari.  You should contact them using the following email address: eb.eg1632144060eilu@16321440603008o1632144060fni1632144060 .

 

1. Lectures

Lesson 1. 03/02/2021. Speaker: Damien Ernst.  Introduction to Reinforcement Learning (RL). Understand how to build a RL agent for non-adversarial environment with discrete state/action spaces.  Podcast lesson 1

Lesson 2. 10/02/2021. Speaker: Damien Ernst.  The Q-learning algorithm (see Slides Lesson 1). Proof related to the upper bound on the suboptimality of \mu_T^* Podcast Lesson 2

Lesson 3. 17/02/2021. Speaker: Damien Ernst. Reinforcement learning for continuous state-action spaces (see Slides Lesson 1).  Discussion  Research paper 1. Podcast Lesson 3

Lesson 4. 24/02/2021. Speaker: Damien Ernst.  Advanced algorithms for learning Q-functions. Podcast Lesson 4

Lesson 5. 03/03/2021. Speaker: Damien Ernst. Discussion assignments, project and presentation Research paper 2.

Lesson 6. 10/03/2021. Speaker:  Adrien Bolland. Gradient-based techniques for reinforcement learning in continuous domains. Slides of the class  –  Watch the video of the class. 

Paper to read for next time: https://papers.nips.cc/paper/1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf

Lesson 7. 17/03/2021. Speaker:  Adrien Bolland. Gradient-based techniques for reinforcement learning in continuous domains. Slides of the class – Watch the video of the class

Lesson 8. 24/03/2021. Speaker: Thibaut Théate. Distributional reinforcement learning. Watch the video of the class

Lesson 9. 31/03/2021. Speaker: Raphael Fonteneau. Advanced batch mode reinforcement learning. Discussion Research paper. 3.

Lesson 10. 21/04/2021. Speaker: Pascal Leroy. Multi-agent reinforcement learning. Watch the video of the class

Lesson 11. 28/04/2021. Speaker: Damien Ernst. Exploration/exploitation in Reinforcement Learning: The multi-armed bandit problems. Class based on a discussion of  Research paper 9 (first 25 pages). (Class cancelled but you can still read the paper 🙂 ).

2. Assignments

Assignment 1 – 03/02/2021. Section 1 to 4 need to be submitted for the 09/02/2021 midnight. Section 5 and 6 for the 16/02/2021 midnight.  Link to the submission platform: https://submit.montefiore.ulg.ac.be/index.php/login

Results Assignment 1

Assignment 2 –  17/02/2021. Section 1 to 4 need to be submitted for the 23/02/2021 midnight. Deadline for the final submission: 02/03/2021 midnight.

Results Assignment 2

Assignment 3 – 17/03/2021 (final assignment). Deadline for the final submission: 27/04/2021.  The assignment is not mandatory but you get a  bonus of +1 on the final note if you get more that 12/20, +2 on the final note get more that 14/20, +3 if more than 16/20 and +4 if you get more than 18/20

Granted Bonus Assignment 3

3. Final project

The final project will be about designing your own intelligent agent to control a double inverted pendulum – a well-known but challenging, chaotic physical system – or a complex energy network – with a large number of states and actions- using any reinforcement learning algorithm which is compatible with continuous state and action spaces.

Deadline for the final report is on the 14/05/2021 midnight

Network management : ANM6-Easy project

Robot equilibrium : Double Inverted Pendulum project

Results Project

4. Evaluations

Due to the COVD-19 crisis, the 5 evaluations that usually took place during the semester have been cancelled. But there will be an oral exam based on all the material we have seen during the class!

[Evaluations that took place during the previous years

Evaluation 1 

Evaluation 2

Evaluation 3

Evaluation 4

Evaluation 5

]

5. Access your results 

Semester grades (without exam)

6. Final exam

Schedule for the final exam

 

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