On the convergence of techniques that improve value iteration
Bibliographic record
Abstract
Prioritisation of Bellman backups or updating only a small subset of actions represent important techniques for speeding up planning in MDPs. The recent literature showed new efficient approaches which exploit these directions. Backward value iteration and backing up only the best actions were shown to lead to a significant reduction of the planning time. This paper conducts a theoretical and empirical analysis of these techniques and shows new important proofs. In particular, (1) it identifies weaker requirements for the convergence of backups based on best actions only, (2) a new method for evaluation of the Bellman error is shown for the update that updates one best action once, (3) it presents the theoretical proof of backward value iteration and establishes required initialisation, (4) and shows that the default state ordering of backups in standard value iteration can significantly influence its performance. Additionally, (5) the existing literature did not compare these methods, either empirically or analytically, against policy iteration. The rigorous empirical and novel theoretical parts of the paper reveal important associations and allow drawing guidelines on which type of value or policy iteration is suitable for a given domain. Finally, our chief message is that standard value iteration can be made far more efficient by simple modifications shown in the paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".