CAMAR：连续动作多智能体路径规划

摘要

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。尽管已提出众多MARL基准测试，但鲜有结合连续状态与动作空间，并包含复杂协调与规划任务的。我们引入CAMAR，一个专为连续动作环境下多智能体路径规划设计的新MARL基准。CAMAR支持智能体间的协作与竞争互动，并能以每秒高达100,000环境步的效率运行。我们还提出了一套三层评估协议，以更好地追踪算法进展，并支持对性能的深入分析。此外，CAMAR允许将RRT和RRT*等经典规划方法整合到MARL流程中，既作为独立基线，又将RRT*与流行MARL算法结合，形成混合方法。我们提供了一系列测试场景与基准工具，确保实验的可重复性与公平比较。实验表明，CAMAR为MARL社区提供了一个既具挑战性又贴近实际的测试平台。

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.

CAMAR：连续动作多智能体路径规划

CAMAR: Continuous Actions Multi-Agent Routing

摘要

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