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Learning to Transfer Role Assignment Across Team Sizes
conference contribution
posted on 2023-02-28, 02:42 authored by D Nguyen, Phuoc NguyenPhuoc Nguyen, Svetha VenkateshSvetha Venkatesh, Truyen TranTruyen TranMulti-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerful approach is to decompose team works into roles, which are ideally assigned to agents with the relevant skills. Training agents to adaptively choose and play emerging roles in a team thus allows the team to scale to complex tasks and quickly adapt to changing environments. These promises, however, have not been fully realised by current role-based multi-agent reinforcement learning methods as they assume either a pre-defined role structure or a fixed team size. We propose a framework to learn role assignment and transfer across team sizes. In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment. We demonstrate that re-using the role-based credit assignment structure can foster the learning process of larger reinforcement learning teams to achieve tasks requiring different roles. Our proposal outperforms competing techniques in enriched role-enforcing Prey-Predator games and in new scenarios in the StarCraft II Micro-Management benchmark.
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Volume
2Pagination
963 - 971ISSN
1548-8403eISSN
1558-2914ISBN-13
9781713854333Title of proceedings
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMASUsage metrics
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