让智能体从历史行为中自适应恢复团队上下文，实现变人数协作。
PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
- 训练时用中心化教师提取团队上下文并个性化分发给各智能体
- 执行时每个智能体从本地历史预测自身上下文，动态调整策略
- 在3个基准上对已知和未知人数均表现更优，适合动态团队场景
协作式多智能体强化学习通常假设执行团队固定，但许多去中心化系统在部署时需面对变动的活跃智能体数量。本文研究周期性团队规模变化场景：每轮由一组同质智能体执行，团队规模跨轮次可变。智能体仅依赖本地历史，不支持执行时通信、特权协调者或在线重训练。因此，有效协作要求每个智能体能从本地信息中恢复活跃团队的相关上下文，并相应调整行为。为此，我们提出PC3D（个性化中心协调上下文蒸馏）方法，训练去中心化策略以从本地交互历史中恢复并使用个性化协调上下文。训练阶段，结构化的中心化教师将活跃团队压缩为协调令牌，并将其个性化为智能体特有上下文，再蒸馏至去中心化策略。执行阶段，每个智能体从本地历史预测自身上下文，并据此自适应地调节决策。在三个协作式MARL基准上，PC3D在已知与未知团队规模下均获得更高回报，消融实验表明性能提升同时归因于上下文蒸馏与自适应使用。
原文摘要 · Abstract (English)
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 retraining. Therefore, effective cooperation requires each agent to recover relevant context about the active team and adapt its behavior accordingly. To this end, we propose PC3D (Personalized Central Coordination Context Distillation), a method for training decentralized policies to recover and use personalized coordination context from local interaction histories. During training, a set-structured centralized teacher compresses the active team into coordination tokens and personalizes them into agent-specific contexts, which are distilled into decentralized policies. At execution, each agent predicts its own context from local history and adaptively uses it to condition decision-making. Across three cooperative MARL benchmarks, PC3D achieves higher returns than the evaluated baselines with both seen and unseen roster sizes, and ablations attribute these gains to both context distillation and adaptive context use.
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