PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.
Decentralized multi-agent reinforcement learning with networked agents: Recent advances.Frontiers of Information Technology & Electronic Engineering, 22(6):802–814
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PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.