CAMAR：連續動作多智能體路徑規劃

摘要

多智能體強化學習（MARL）是解決合作與競爭決策問題的強大範式。儘管已提出許多MARL基準測試，但鮮有結合連續狀態與動作空間並具備挑戰性協調與規劃任務的基準。我們引入了CAMAR，這是一個專為連續動作環境中的多智能體路徑規劃設計的新MARL基準。CAMAR支持智能體之間的合作與競爭互動，並能以每秒高達100,000個環境步驟的效率運行。我們還提出了一個三層評估協議，以更好地追蹤算法進展並實現更深入的性能分析。此外，CAMAR允許將經典規劃方法如RRT和RRT*整合到MARL流程中。我們將它們作為獨立基線，並將RRT*與流行的MARL算法結合，創建混合方法。我們提供了一套測試場景和基準測試工具，以確保可重現性和公平比較。實驗表明，CAMAR為MARL社群提供了一個具有挑戰性且真實的測試平台。

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：連續動作多智能體路徑規劃

CAMAR: Continuous Actions Multi-Agent Routing

摘要

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