This class will be given during the second semester on Wednesday afternoon in Room S36 of Building B37. It starts at 2:00pm. The first class is on the 10 th of February.  The teaching assistant for the class is Mr. Samy Aittahar  (Email: eb.eg1606611833eilu@1606611833rahat1606611833tias1606611833 ).

Students should come to the class with their laptop.

1. Lectures

Lesson 1. 05/02/2020. Speaker: Damien Ernst.  Introduction to Reinforcement Rearning (RL). Understand how to build a RL agent for non-adversarial environment with discrete state/action spaces.

Lesson 2. 12/02/2020. Speaker: Damien Ernst.  The Q-learning algorithm (see Slides Lesson 1).

Lesson 3. 19/02/2020. Speaker: Damien Ernst. Reinforcement learning for continuous state-action spaces (see Slides Lesson 1).

Lesson 4. 26/02/2020. Speaker: Damien Ernst. Discussion  Research paper 1.

Lesson 5. 04/03/2020. Speaker: Raphael Fonteneau. Advanced batch mode reinforcement learning. Discussion Research paper 2.

Lesson 6. 11/03/2020. Speaker: Damien Ernst.  Advanced algorithms for learning Q-functions. Discussion Research paper 3.

=== The program hereafter will be modified due to the coronavirus crisis ===

Lesson 7. 18/03/2020. Speaker:  Samy Aittahar. Introduction to Gradient-based Policy Search.   Discussion Research paper 4 , Research paper 5 , Research paper 6 .

Lesson 8. 25/03/2020. Speaker: Nicolas Vecoven, Pascal Leroy, Amina  Benzerga. A glimpse at the research done in RL at the Montefiore Institute. Discussion Research paper 7 .

Lesson 9. 01/04/2020. Reinforcement learning in the energy industry. Real applications. Bert Claessens (Restore).

Lesson 10. 22/04/2020. 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).



2. Assignments

Assignment 1 – 05/02/2020. Section 1 to 4 need to be submitted for the 11/02/2020 midnight. Section 5 and 6 for the 18/02/2020.  Link to the submission platform:

Results assignments 1

Assignment 2 – 26/02/2020. Section 1 to 4 need to be submitted for the 06/03/2020 midnight. Deadline for the final submission: 26/03/2020 midnight.

Assignment 3 – 17/03/2020 (final assignment). Deadline for the final submission: 15/05/2020.  Assignment 3 is  not mandatory. But based on the quality of the work, you will get a bonus.

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 – using any reinforcement learning algorithm which is compatible with continuous state and action spaces.

Deadline for the final report is on the 15/05/2020. Presentation of your results: to be disucssed.

Download your final project


Schedule for the project defense

4. Evaluations

There will be a total of 5  evaluations.

Evaluation 1 

Evaluation 2

Evaluation 3

Evaluation 4

Evaluation 5

5. Access your results 

Access your results

6. Final exam 

Schedule for the final exam


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