Multi-Agent Policy Transfer via Task Relationship Modeling

• URL: http://arxiv.org/abs/2203.04482v1
• Date: Wed, 9 Mar 2022 01:49:21 GMT
• Title: Multi-Agent Policy Transfer via Task Relationship Modeling
• Authors: Rongjun Qin, Feng Chen, Tonghan Wang, Lei Yuan, Xiaoran Wu, Zongzhang Zhang, Chongjie Zhang, Yang Yu
• Abstract summary: We try to discover and exploit common structures among tasks for more efficient transfer. We propose to learn effect-based task representations as a common space of tasks, using an alternatively fixed training scheme. As a result, the proposed method can help transfer learned cooperation knowledge to new tasks after training on a few source tasks.
• Score: 28.421365805638953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous work on multi-agent transfer learning accommodate teams of different sizes, heavily relying on the generalization ability of neural networks for adapting to unseen tasks. We believe that the relationship among tasks provides the key information for policy adaptation. In this paper, we try to discover and exploit common structures among tasks for more efficient transfer, and propose to learn effect-based task representations as a common space of tasks, using an alternatively fixed training scheme. We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks. As a result, the proposed method can help transfer learned cooperation knowledge to new tasks after training on a few source tasks. We also find that fine-tuning the transferred policies help solve tasks that are hard to learn from scratch.

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