AI RESEARCH
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
arXiv CS.LG
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ArXi:2312.03644v3 Announce Type: replace Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learning in MARL offers flexibility and scalability, accurately assigning credit to individual agents in offline settings poses challenges because interactions with an environment are prohibited. In this paper, we propose a new framework, namely Multi-Agent Causal Credit Assignment (MACCA), to address credit assignment in the offline MARL setting.