The Heterogeneous Multi-Agent Challenge

• URL: http://arxiv.org/abs/2509.19512v1
• Date: Tue, 23 Sep 2025 19:30:30 GMT
• Title: The Heterogeneous Multi-Agent Challenge
• Authors: Charles Dansereau, Junior-Samuel Lopez-Yepez, Karthik Soma, Antoine Fagette,
• Abstract summary: Multi-Agent Reinforcement Learning (MARL) is a growing research area which gained significant traction in recent years.<n> Heterogeneous Multi-Agent Reinforcement Learning (HeMARL) is where agents with different sensors, resources, or capabilities must cooperate based on local information.
• Score: 0.4199844472131922
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-Agent Reinforcement Learning (MARL) is a growing research area which gained significant traction in recent years, extending Deep RL applications to a much wider range of problems. A particularly challenging class of problems in this domain is Heterogeneous Multi-Agent Reinforcement Learning (HeMARL), where agents with different sensors, resources, or capabilities must cooperate based on local information. The large number of real-world situations involving heterogeneous agents makes it an attractive research area, yet underexplored, as most MARL research focuses on homogeneous agents (e.g., a swarm of identical robots). In MARL and single-agent RL, standardized environments such as ALE and SMAC have allowed to establish recognized benchmarks to measure progress. However, there is a clear lack of such standardized testbed for cooperative HeMARL. As a result, new research in this field often uses simple environments, where most algorithms perform near optimally, or uses weakly heterogeneous MARL environments.

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