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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…