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Schrittwieser and colleagues: MuZero planning with a learned model

Contribution grade

Reinforcement learning · METHOD

Reference score85,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Planned with a learned reward/policy/value model and reported strong Atari and board-game results.

Scientific scope

The record is the exact twelve-author Nature paper. Claims remain confined to the reported games, training budgets and evaluation protocol; it does not establish general real-world planning, safety or domain-independent intelligence.

Evidence

The linked sources identify this contribution and its scope. WRS has not independently reconstructed the scientific claims. The record is the exact twelve-author Nature paper. Claims remain confined to the reported games, training budgets and evaluation protocol; it does not establish general real-world planning, safety or domain-independent intelligence.

Contributors

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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