Randomized entity-wise factorization for multi-agent reinforcement learning
Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities. Our method aims to leverage these commonalities by asking the question: “What is the expected...
| 主要な著者: | , , , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
PMLR
2021
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