Off-policy TD(λ) with a true online equivalence
Bibliographic record
Abstract
Van Seijen and Sutton (2014) recently proposed a new version of the linear TD(λ) learning algorithm that is exactly equivalent to an online forward view and that empirically performed better than its classical counterpart in both prediction and control problems. However, their algorithm is restricted to on-policy learning. In the more general case of off-policy learning, in which the policy whose outcome is predicted and the policy used to generate data may be different, their algorithm cannot be applied. One reason for this is that the algorithm bootstraps and thus is subject to instability problems when function approximation is used. A second reason true online TD(λ) cannot be used for off-policy learning is that the off-policy case requires sophisticated importance sampling in its eligibility traces. To address these limitations, we generalize their equivalence result and use this generalization to construct the first online algorithm to be exactly equivalent to an off-policy forward view. We show this algorithm, named true online GTD(λ), empirically outperforms GTD(λ) (Maei, 2011) which was derived from the same objective as our forward view but lacks the exact online equivalence. In the general theorem that allows us to derive this new algorithm, we encounter a new general eligibility-trace update.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".