CAMAR: Continuous Actions Multi-Agent Routing

Artem Pshenitsyn; Aleksandr Panov; Alexey Skrynnik

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arXiv · 2508.12845

CAMAR: Continuous Actions Multi-Agent Routing

Artem Pshenitsyn·Aleksandr Panov·Alexey Skrynnik

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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.

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BibTeX RIS

Artem Pshenitsyn, Aleksandr Panov, Alexey Skrynnik. 2025-08-18. CAMAR: Continuous Actions Multi-Agent Routing. https://arxiv.org/abs/2508.12845

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