Abstract

Introduction to the models and mathematical tools used in formalizing the problem of learning and decision-making under uncertainty. In particular, we will focus on the frameworks of reinforcement learning and multi-arm bandit. The main topics studied during the course are:

-Historical multi-disciplinary basis of reinforcement learning

-Markov decision processes and dynamic programming

-Stochastic approximation and Monte-Carlo methods

-Introduction to stochastic and adversarial multi-arm bandit

-Approximate dynamic programming

Where and When

The course on “Reinforcement Learning” will be held at the Ecole Centrale de Lille. The room for lectures is B7-14 and for the practical sessions is C016.

Schedule

See hyperplanning.

Lectures

News

1. • Text of the first Homework: homework1-tree.pdf
   

2. • Text of the second Homework: homework2.pdf code.zip
   

3. • Text of the third Homework: homework3.pdf code.zip
   

4. • Schedule for the presentations: schedule.pdf

Proposed papers to review

RULES: Students should work in pairs and prepare a presentation of 15 minutes on two papers (one paper is also acceptable if particularly long) chosen in the following list.

1. • Google study on multi-arm bandit for Google Analytics
   

2. •“Adaptive Stochastic Control for Smart Grids”
   

3. •“An Intelligent Battery Controller Using Bias-Corrected Q-learning”
   

4. •“An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application”
   

5. •“A Contextual-Bandit Approach to Personalized News Article Recommendation”
   

6. •“Reinforcement Learning in Robotics: A Survey”
   

7. •“Autonomous inverted helicopter flight via reinforcement learning”
   

8. •“Reinforcement Learning for Optimized Trade Execution”
   

9. •“Reinforcement Learning-based Control of Traffic Lights in Non-stationary Environments”
   

10. •“Optimizing Dialogue Management with Reinforcement Learning”
    

11. •“Coadaptive Brain–Machine Interface via Reinforcement Learning”
    

12. •“RL-MAC: a reinforcement learning based MAC protocol for wireless sensor networks”
    

13. •“Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems”
    

14. •“Interactive Selection of Visual Features through Reinforcement Learning”
    

15. •“Least-Squares Policy Iteration”
    

16. •“Regret Minimization in Games with Incomplete Information”
    

17. •“Approximate Dynamic Programming Finally Performs Well in the Game of Tetris”
    

18. •“Playing Atari with Deep Reinforcement Learning”
    

19. •“Reinforcement Learning for Elevator Control”
    

20. •John Moody and Matthew Saffell. Learning to trade via direct reinforcement, 2001
    

21. •Beomsoo Park and Benjamin Van Roy. Adaptive execution: Exploration and learning of price impact
    

22. •Ying Tan, Wei Liu, and Qinru Qiu. Adaptive power management using reinforcement learning
    

23. •J. Mary, R. Gaudel, Ph. Preux, Bandits Warm-up Cold Recommender Systems
    

24. •