新基准CAMAR让多智能体在连续动作下高效路径规划，支持合作与竞争。
CAMAR: Continuous Actions Multi-Agent Routing
- 设计连续动作空间下的多智能体路径规划基准，支持高并发仿真。
- 每秒可运行10万步环境交互，性能达现有基准的数十倍。
- 兼容经典规划算法如RRT*，可构建混合强化学习方案。
多智能体强化学习（MARL）是解决协作与竞争决策问题的强大范式。尽管已有诸多MARL基准，但能同时结合连续状态与动作空间，并具备复杂协调与规划任务的仍较少。本文提出CAMAR，一个专为连续动作环境下多智能体路径寻址设计的新基准。该基准支持智能体间的协作与竞争，可实现每秒高达10万次环境步骤的高效运行。我们还设计了三级评估协议，以更精细跟踪算法进展并深化性能分析。CAMAR支持将经典规划方法如RRT和RRT*集成至MARL流程中：既可作为独立基线，也可与主流MARL算法结合形成混合策略。我们提供一套完整测试场景与基准工具，确保结果可复现且公平比较。实验表明，CAMAR为MARL社区提供了挑战性强且贴近现实的评测平台。
原文摘要 · Abstract (English)
Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at up to 100,000 environment steps per second. We also propose a three-tier evaluation protocol to better track algorithmic progress and enable deeper analysis of performance. In addition, CAMAR allows the integration of classical planning methods such as RRT and RRT* into MARL pipelines. We use them as standalone baselines and combine RRT* with popular MARL algorithms to create hybrid approaches. We provide a suite of test scenarios and benchmarking tools to ensure reproducibility and fair comparison. Experiments show that CAMAR presents a challenging and realistic testbed for the MARL community.
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