The Heterogeneous Multi-Agent Challenge
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911173043027968 |
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| author | Dansereau, Charles Lopez-Yepez, Junior-Samuel Soma, Karthik Fagette, Antoine |
| author_facet | Dansereau, Charles Lopez-Yepez, Junior-Samuel Soma, Karthik Fagette, Antoine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19512 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Heterogeneous Multi-Agent Challenge Dansereau, Charles Lopez-Yepez, Junior-Samuel Soma, Karthik Fagette, Antoine Multiagent Systems Artificial Intelligence 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. |
| title | The Heterogeneous Multi-Agent Challenge |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19512 |