Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909444910088192 |
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| author | Xue, Ke Wang, Yutong Guan, Cong Yuan, Lei Fu, Haobo Fu, Qiang Qian, Chao Yu, Yang |
| author_facet | Xue, Ke Wang, Yutong Guan, Cong Yuan, Lei Fu, Haobo Fu, Qiang Qian, Chao Yu, Yang |
| contents | Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may be inefficient. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on various heterogeneous tasks highlight the necessity of considering the heterogeneous setting and demonstrate that our proposed method is a promising solution for heterogeneous ZSC tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_04957 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution Xue, Ke Wang, Yutong Guan, Cong Yuan, Lei Fu, Haobo Fu, Qiang Qian, Chao Yu, Yang Neural and Evolutionary Computing Artificial Intelligence Machine Learning Multiagent Systems Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may be inefficient. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on various heterogeneous tasks highlight the necessity of considering the heterogeneous setting and demonstrate that our proposed method is a promising solution for heterogeneous ZSC tasks. |
| title | Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2208.04957 |