3 papers
cs.MA2025
Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
Maxime Toquebiau, Jae-Yun Jun, Faïz Benamar +1
Most prior works on communication in multi-agent reinforcement learning have focused on emergent communication, which often results in inefficient and non-interpretable systems. In…
cs.MA2024
Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning
Maxime Toquebiau, Nicolas Bredeche, Faïz Benamar +1
Multi-agent deep reinforcement learning (MADRL) problems often encounter the challenge of sparse rewards. This challenge becomes even more pronounced when coordination among agents…
cs.GT2024
Is Limited Information Enough? An Approximate Multi-agent Coverage Control in Non-Convex Discrete Environments
Tatsuya Iwase, Aurélie Beynier, Nicolas Bredeche +2
Conventional distributed approaches to coverage control may suffer from lack of convergence and poor performance, due to the fact that agents have limited information, especially i…