AWhat changed
Discretized trajectory dimensions, modeled joint state/action/reward sequences autoregressively and used beam search with reward-based scoring for receding-horizon control.
Scientific scope
Discretized trajectory dimensions, modeled joint state/action/reward sequences autoregressively and used beam search with reward-based scoring for receding-horizon control. Unlike Decision Transformer, this contribution learns a trajectory model and performs search. Discretization, finite context and finite beam width remain resource/approximation constraints; beam search is not a global-optimality certificate. Neither contribution is credited with inventing Transformers.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Unlike Decision Transformer, this contribution learns a trajectory model and performs search. Discretization, finite context and finite beam width remain resource/approximation constraints; beam search is not a global-optimality certificate. Neither contribution is credited with inventing Transformers.
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.
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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