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Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
This paper studies multi-agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a local categorical policy, so feasible joint actions form a partition matroid over agent-action pairs. Classical multiline
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