CAMAR: Continuous Actions Multi-Agent Routing
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.
68a5c37fc4b3ea17d2d2cfff
Artem Pshenitsyn
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2025-08-20T19:11:42.553Z
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/avatars/e11767ad04c26fa84966017d5c43dbaa.svg
Artem Pshenitsyn
user
Square596
68a5c37fc4b3ea17d2d2d000
Aleksandr Panov
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Alexey Skrynnik
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2025-09-04T08:48:01.874Z
652ced57756a15d750266362
/avatars/5ef33e4935ccc6e930ceb2475c270bb1.svg
Alexey Skrynnik
user
tviskaron
CAMAR: Continuous Actions Multi-Agent Routing
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 is a new MARL benchmark for continuous action pathfinding that supports efficient evaluation and hybrid approaches with classical planning methods.
multi-agent reinforcement learning MARL continuous state and action spaces multi-agent pathfinding three-tier evaluation protocol RRT RRT* hybrid approaches test scenarios benchmarking tools