GQ( ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces
Bibliographic record
Abstract
A new family of gradient temporal-difference learning algorithms have recently been introduced by Sutton, Maei and others in which function approximation is much more straightforward. In this paper, we introduce the GQ() algorithm which can be seen as extension of that work to a more general setting including eligibility traces and off-policy learning of temporally abstract predictions. These extensions bring us closer to the ultimate goal of this work-the development of a universal prediction learning algorithm suitable for learning experientially grounded knowledge of the world. Eligibility traces are essential to this goal because they bridge the temporal gaps in cause and effect when experience is processed at a temporally fine resolution. Temporally abstract predictions are also essential as the means for representing abstract, higher-level knowledge about courses of action, or options. GQ() can be thought of as an extension of Q-learning. We extend existing convergence results for policy evaluation to this setting and carry out a forward-view/backwardview analysis to derive and prove the validity of the new algorithm.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".