PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation

ArXi:2605.10377v1 Announce Type: new Cooperative multi-agent reinforcement learning often assumes a fixed execution team, yet many decentralized systems must operate with varying numbers of active agents during deployment. We study this setting under episodic roster variation: each episode is executed by a set of homogeneous agents, with the team size varying across episodes. Agents act only from local histories, without execution-time communication, privileged coordinators, or online re