Variational Offline Multi-agent Skill Discovery

• URL: http://arxiv.org/abs/2405.16386v2
• Date: Tue, 15 Oct 2024 04:08:33 GMT
• Title: Variational Offline Multi-agent Skill Discovery
• Authors: Jiayu Chen, Bhargav Ganguly, Tian Lan, Vaneet Aggarwal,
• Abstract summary: We propose two novel auto-encoder schemes to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills. Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining.
• Score: 43.869625428099425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability. Despite extensive research, research gaps remain in multi-agent scenarios, particularly for automatically extracting subgroup coordination patterns in a multi-agent task. In this case, we propose two novel auto-encoder schemes: VO-MASD-3D and VO-MASD-Hier, to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills, which firstly solves the aforementioned challenge. An essential algorithm component of these schemes is a dynamic grouping function that can automatically detect latent subgroups based on agent interactions in a task. Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining. Empirical evaluations on StarCraft tasks indicate that our approach significantly outperforms existing hierarchical multi-agent reinforcement learning (MARL) methods. Moreover, skills discovered using our method can effectively reduce the learning difficulty in MARL scenarios with delayed and sparse reward signals.

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