CAMAR: Continuous Actions Multi-Agent Routing
Authors: name: Artem Pshenitsyn, name: Aleksandr Panov, name: Alexey Skrynnik

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.