arxivcs.MAcs.LG2026-07-22
Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning
Taisuke Takayama, Naoto Yoshida, Tadahiro Taniguchi
In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representation learning-based approaches enable decentralized agents to learn messages grounded in their own observations, but they rely onl…