Bayesian Action Decoder for cooperative multi-agent learning

What is recorded here?

Chooses deterministic partial policies using public information and shared randomness, then updates public beliefs from observed actions to support implicit communication.

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Scientific scope

Common-knowledge feature partition and policy structure are assumed; practical factorized policies and approximate beliefs are not exact general belief-state inference. First two authors contribute equally.

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Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

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People and systems

Jakob N. Foerster; H. Francis Song; Edward Hughes; Neil Burch; Iain Dunning; Shimon Whiteson; Matthew M. Botvinick; Michael Bowling

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English catalogue narrative