为异构多智能体强化学习设计标准化评测基准
The Heterogeneous Multi-Agent Challenge
- 提出首个面向协作式异构多智能体的统一测试平台
- 填补现有研究在复杂异构场景下的评估空白
- 适合多智能体系统与分布式决策研究者使用
多智能体强化学习（MARL）是近年来迅速发展的研究领域，已将深度强化学习应用扩展至更广泛的问题。其中，异构多智能体强化学习（HeMARL）尤为挑战：不同感知能力、资源或功能的智能体需基于局部信息协作。现实中大量场景涉及异构智能体，该领域极具研究价值却尚未充分探索，因多数MARL研究集中于同质智能体（如一群相同机器人）。在MARL与单智能体强化学习中，已有类似ALE和SMAC的标准环境作为公认基准。然而，协作式异构MARL尚无统一评测平台，导致新研究多采用简单环境（算法表现接近最优）或弱异构环境，难以真实反映算法性能。
原文摘要 · Abstract (English)
Multi-Agent Reinforcement Learning (MARL) is a growing research area which gained significant traction in recent years, extending Deep RL applications to a much wider range of problems. A particularly challenging class of problems in this domain is Heterogeneous Multi-Agent Reinforcement Learning (HeMARL), where agents with different sensors, resources, or capabilities must cooperate based on local information. The large number of real-world situations involving heterogeneous agents makes it an attractive research area, yet underexplored, as most MARL research focuses on homogeneous agents (e.g., a swarm of identical robots). In MARL and single-agent RL, standardized environments such as ALE and SMAC have allowed to establish recognized benchmarks to measure progress. However, there is a clear lack of such standardized testbed for cooperative HeMARL. As a result, new research in this field often uses simple environments, where most algorithms perform near optimally, or uses weakly heterogeneous MARL environments.
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