Bibliographic record
Abstract
In this paper we introduce a multi-step linear Dyna-style planning algorithm. The key element of the multi-step linear Dyna is a multi-step linear model that enables multi-step projection of a sampled feature and multi-step planning based on the simulated multi-step transition experience. We propose two multi-step linear models. The first iterates the one-step linear model, but is generally computationally complex. The second interpolates between the one-step model and the infinite-step model (which turns out to be the LSTD solution), and can be learned efficiently online. Policy evaluation on Boyan Chain shows that multi-step linear Dyna learns a policy faster than single-step linear Dyna, and generally learns faster as the number of projection steps increases. Results on Mountain-car show that multi-step linear Dyna leads to much better online performance than single-step linear Dyna and model-free algorithms; however, the performance of multi-step linear Dyna does not always improve as the number of projection steps increases. Our results also suggest that previous attempts on extending LSTD for online control were unsuccessful because LSTD looks infinite steps into the future, and suffers from the model errors in non-stationary (control) environments. 1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".