TMAC: a Transformer-based partially observable multi-agent communication method
Effective communication plays a crucial role in coordinating the actions of multiple agents. Within the realm of multi-agent reinforcement learning, agents have the ability to share information with one another through communication channels, leading to enhanced learning outcomes and successful goal...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2758.pdf |
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| _version_ | 1849733009318608896 |
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| author | Xuesi Li Shuai Xue Ziming He Haobin Shi |
| author_facet | Xuesi Li Shuai Xue Ziming He Haobin Shi |
| author_sort | Xuesi Li |
| collection | DOAJ |
| description | Effective communication plays a crucial role in coordinating the actions of multiple agents. Within the realm of multi-agent reinforcement learning, agents have the ability to share information with one another through communication channels, leading to enhanced learning outcomes and successful goal attainment. Agents are limited by their observations and communication ranges due to increasingly complex location arrangements, making multi-agent collaboration based on communication increasingly difficult. In this article, for multi-agent communication in some partially observable scenarios, we propose a Transformer-based Partially Observable Multi-Agent Communication algorithm (TMAC), which improves agents extracting features and generating output messages. Meanwhile, a self-message fusing module is proposed to obtain features from multiple sources. Therefore, agents can achieve better collaboration through communication. At the same time, we performed experimental verification in the surviving and the StarCraft Multi-Agent Challenge (SMAC) environments where agents had limited local observation and could only communicate with neighboring agents. In two test environments, our method achieves an improvement in performance 6% and 10% over the baseline algorithm, respectively. Our code is available at https://gitee.com/xs-lion/tmac. |
| format | Article |
| id | doaj-art-df3e29c9839d4c328d3167176fde8f38 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-df3e29c9839d4c328d3167176fde8f382025-08-20T03:08:09ZengPeerJ Inc.PeerJ Computer Science2376-59922025-04-0111e275810.7717/peerj-cs.2758TMAC: a Transformer-based partially observable multi-agent communication methodXuesi LiShuai XueZiming HeHaobin ShiEffective communication plays a crucial role in coordinating the actions of multiple agents. Within the realm of multi-agent reinforcement learning, agents have the ability to share information with one another through communication channels, leading to enhanced learning outcomes and successful goal attainment. Agents are limited by their observations and communication ranges due to increasingly complex location arrangements, making multi-agent collaboration based on communication increasingly difficult. In this article, for multi-agent communication in some partially observable scenarios, we propose a Transformer-based Partially Observable Multi-Agent Communication algorithm (TMAC), which improves agents extracting features and generating output messages. Meanwhile, a self-message fusing module is proposed to obtain features from multiple sources. Therefore, agents can achieve better collaboration through communication. At the same time, we performed experimental verification in the surviving and the StarCraft Multi-Agent Challenge (SMAC) environments where agents had limited local observation and could only communicate with neighboring agents. In two test environments, our method achieves an improvement in performance 6% and 10% over the baseline algorithm, respectively. Our code is available at https://gitee.com/xs-lion/tmac.https://peerj.com/articles/cs-2758.pdfMulti-agent reinforcement learningCommunicationAttention mechanism |
| spellingShingle | Xuesi Li Shuai Xue Ziming He Haobin Shi TMAC: a Transformer-based partially observable multi-agent communication method PeerJ Computer Science Multi-agent reinforcement learning Communication Attention mechanism |
| title | TMAC: a Transformer-based partially observable multi-agent communication method |
| title_full | TMAC: a Transformer-based partially observable multi-agent communication method |
| title_fullStr | TMAC: a Transformer-based partially observable multi-agent communication method |
| title_full_unstemmed | TMAC: a Transformer-based partially observable multi-agent communication method |
| title_short | TMAC: a Transformer-based partially observable multi-agent communication method |
| title_sort | tmac a transformer based partially observable multi agent communication method |
| topic | Multi-agent reinforcement learning Communication Attention mechanism |
| url | https://peerj.com/articles/cs-2758.pdf |
| work_keys_str_mv | AT xuesili tmacatransformerbasedpartiallyobservablemultiagentcommunicationmethod AT shuaixue tmacatransformerbasedpartiallyobservablemultiagentcommunicationmethod AT ziminghe tmacatransformerbasedpartiallyobservablemultiagentcommunicationmethod AT haobinshi tmacatransformerbasedpartiallyobservablemultiagentcommunicationmethod |