Portrait de Julien Armand

Julien Armand

julien.armand@mila.quebec
Maîtrise recherche - Université Laval
Superviseur⋅e principal⋅e
Audrey Durand
Sujets de recherche
Apprentissage par renforcement
Systèmes multi-agents

Publications

Multi-Agent Matrix Games with Individual learners: How Exploration-Exploitation Strategies Impact the Emergence of Coordination
Julien Armand
Tommy Chien-Hsuan Lin
Maxime Heuillet
Audrey Durand
Coordination between independent learning agents in a multi-agent environment is an important problem where AI systems may impact each other… (voir plus)s learning process. In this paper, we study how individual agents converge to optimal equilibrium in multi-agent where coordination is necessary to achieve optimality. Specifically, we cover the case of coordination to maximize every individual payoffs and coordination to maximize the collective payoff (cooperation). We study the emergence of such coordination behaviours in two-players matrix games with unknown payoff matrices and noisy bandit feedback. We consider five different environments along with widely used deterministic and stochastic bandit strategies. We study how different learning strategies and observation noise influence convergence to the optimal equilibrium. Our results indicate that coordination often emerge more easily from interactions between deterministic agents, especially when they follow the same learning behaviour. However, stochastic learning strategies appear to be more robust in the presence of many optimal joint actions. Overall, noisy observations often help stabilizing learning behaviours.
2025-06-22
rl-conference.cc/RLC/2025/Workshop/CoCoMARL (poster)
openreview.net
openreview.net