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• W3119697935 abstract "We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication. We quantify the amount of communication as the maximum degree of the communication graph; this metric captures settings where agents have limited bandwidth. Minimizing communication is challenging due to the combinatorial nature of both the decision space and the objective; for instance, we cannot solve this problem by training neural networks using gradient descent. We propose a novel algorithm that synthesizes a control policy that combines a programmatic communication policy used to generate the communication graph with a transformer policy network used to choose actions. Our algorithm first trains the transformer policy, which implicitly generates a soft communication graph; then, it synthesizes a programmatic communication policy that hardens this graph, forming a neurosymbolic transformer. Our experiments demonstrate how our approach can synthesize policies that generate low-degree communication graphs while maintaining near-optimal performance." @default.
• W3119697935 created "2021-01-18" @default.
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• W3119697935 date "2021-01-05" @default.
• W3119697935 modified "2023-09-25" @default.
• W3119697935 title "Neurosymbolic Transformers for Multi-Agent Communication" @default.
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