Multi Agent Reinforcement Learning
Caution, these contents corresponds to the coursebooks of last year
EE-806 / 2 credits
Teacher: Cevher Volkan
Language: English
Remark: 29 to 31 July 2024; Information and Registration via https://sites.google.com/view/marl-school2024/apply?authuser=0
Frequency
Only this year
Summary
The goal of the summer school are providing a rigorous introduction to the foundations of MARL and highlight the challenges that arise in the modern research directions in this area.
Content
- Tutorial 1: Game Theory
- Tutorial 2: Adversarial MDP
- Tutorial 3: Multi Agent RL
- Research Talk 1: Optimistic exploration in Multi Agent RL
- Research Talk 2: Optimization for Multi Agent RL
- Research Talk 3: Applications of Multi Agent RL
- Research Talk 4: Safety in Multi Agent RL
- Research Talk 5: Multi Agent Imitation Learning
Keywords
Reinforcement Learning, Machine Learning, Multi Agent.
Assessment methods
Poster presentation.
In the programs
- Number of places: 30
- Exam form: Oral (session free)
- Subject examined: Multi Agent Reinforcement Learning
- Lecture: 15 Hour(s)
- Type: optional