cs.AI updates on arXiv.org 13小时前
CAMAR: Continuous Actions Multi-Agent Routing
index_new5.html
../../../zaker_core/zaker_tpl_static/wap/tpl_guoji1.html

 

本文介绍了一种名为CAMAR的新型多智能体强化学习基准,旨在解决连续动作空间中的路径寻找问题。CAMAR支持合作与竞争交互,并支持将经典规划方法集成到MARL中,提供了一系列测试场景和基准测试工具,为MARL社区提供挑战和现实测试环境。

arXiv:2508.12845v1 Announce Type: new Abstract: 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.

Fish AI Reader

Fish AI Reader

AI辅助创作,多种专业模板,深度分析,高质量内容生成。从观点提取到深度思考,FishAI为您提供全方位的创作支持。新版本引入自定义参数,让您的创作更加个性化和精准。

FishAI

FishAI

鱼阅,AI 时代的下一个智能信息助手,助你摆脱信息焦虑

联系邮箱 441953276@qq.com

相关标签

多智能体强化学习 连续动作环境 路径寻找 基准测试 CAMAR
相关文章