This class will be given during the second semester on Wednesday afternoon in Room S.39 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.eg1580230766eilu@1580230766rahat1580230766tias1580230766 ).

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: Damien Ernst.  Advanced algorithms for learning Q-functions. Discussion Research paper 2.

Lesson 6. 11/03/2020. Speaker: Raphael Fonteneau. Advanced batch mode reinforcement learning. Discussion Research paper 5.

Lesson 7. 18/03/2020. Speaker:  Samy Aittahar. Introduction to Gradient-based Policy Search.   Discussion Research paper 3 and Research paper 4.

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 6.

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

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 7 (first 25 pages).



2. Assignments

Assignment 1 – 05/02/2020. Section 1 to 3 need to be submitted for the 11/02/2020 midnight. Section 4 and 5 for the 18/02/2020. Deadline for the final submission: 25/02/2020 midnight. 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 – 11/03/2020 (final assignment). Deadline for the final submission: 15/05/2020.  Assignment 3 is not mandatory. But if you get between 10/20-15/20 for the assignment, you get a +2 bonus on your final note, and a plus +4 bonus if you are above 15 and below 20. If you get 20/20, you get a plus 6 bonus.

3. Final project

The final project will be about designing your own intelligent agent for a video game (Starcraft II ; just kidding) using any reinforcement learning technique you want. You have to report the performances of your algorithms according to the number of episodes played by your agent. And the faster it learns, the higher your note for the project will be.

Deadline for the final report is on the 04/05/2020. Presentation of your results on the 06/07/2019.

Download your final project

Link to the source code defining the environment

Schedule for the project defense

4. Evaluations

There will be a total of 5  evaluations.

Evaluation 1 – Results

Evaluation 2 – Results

Evaluation 3 – Results

Evaluation 4 – Results

Evaluation 5 – Results

5. Final exam 

Schedule for the final exam


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