{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:46:45Z","timestamp":1769640405231,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>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. In this paper, we address this gap by proposing the Heterogeneous Multi-Agent Challenge (HeMAC) (Code is available at: https:\/\/github.com\/ThalesGroup\/hemac), a new benchmarking environment based on the PettingZoo standard. HeMAC features a suite of challenges across multiple scenarios, offering varied and controllable complexity and agent heterogeneity. Our results show that while agents using advanced algorithms such as MAPPO excel in simpler cooperative tasks, their performance declines as heterogeneity increases, with IPPO outperforming them in highly diverse scenarios. QMIX struggles significantly under these conditions due to its assumptions of shared action values and agent homogeneity. These findings demonstrate HeMAC\u2019s value as a rigorous testbed for evaluating MARL algorithms in heterogeneous settings, and emphasize the need for further research in this field to handle complexity and heterogeneity effectively.<\/jats:p>","DOI":"10.3233\/faia251197","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:17Z","timestamp":1761126857000},"source":"Crossref","is-referenced-by-count":1,"title":["The Heterogeneous Multi-Agent Challenge"],"prefix":"10.3233","author":[{"given":"Charles","family":"Dansereau","sequence":"first","affiliation":[{"name":"THALES, cortAIx Labs Canada"}]},{"given":"Junior Samuel","family":"Lopez Yepez","sequence":"additional","affiliation":[{"name":"THALES, cortAIx Labs Canada"}]},{"given":"Karthik","family":"Soma","sequence":"additional","affiliation":[{"name":"THALES, cortAIx Labs Canada"}]},{"given":"Antoine","family":"Fagette","sequence":"additional","affiliation":[{"name":"THALES, cortAIx Labs Canada"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:17Z","timestamp":1761126857000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251197","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}