Bayesian Action Decoder for cooperative multi-agent learning

What changed

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

Scientific scope

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

Evidence

The linked original sources describe this contribution and its evidence limits. WRS has not independently reconstructed the full scientific claims. 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.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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