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- W1745487673 abstract "In this paper, we discuss Profit-sharing, an experience-based reinforcement learning approach (which is similar to a Monte-Carlo based reinforcement learning method) that can be used to learn robust and effective actions within uncertain, dynamic, multi-agent systems. We introduce the cut-loop routine that discards looping behavior, and demonstrate its effectiveness empirically within a simplified NEO (non-combatant evacuation operation) domain. This domain consists of several agents which ferry groups of evacuees to one of several shelters. We demonstrate that the cut-loop routine makes the Profit-sharing approach adaptive and robust within a dynamic and uncertain domain, without the need for predefined knowledge or subgoals. We also compare it empirically with the popular Q-learning approach." @default.
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- W1745487673 date "2000-01-01" @default.
- W1745487673 modified "2023-10-16" @default.
- W1745487673 title "Experience-Based Reinforcement Learning to Acquire Effective Behavior in a Multi-agent Domain" @default.
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- W1745487673 doi "https://doi.org/10.1007/3-540-44533-1_16" @default.
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