A new Q(lambda) with interim forward view and Monte Carlo equivalence
Bibliographic record
Abstract
Q-learning, the most popular of reinforcement learning algorithms, has always included an extension to eligibility traces to enable more rapid learning and improved asymptotic performance on non-Markov problems. The λ parameter smoothly shifts on-policy algorithms such as TD(λ) and Sarsa(λ) from a pure bootstrapping form (λ=0) to a pure Monte Carlo form (λ=1). In off-policy algorithms, including Q(λ), GQ(λ), and off-policy LSTD(λ), the λ parameter is intended to play the same role, but does not; on every exploratory action these algorithms bootstrap regardless of the value of λ, and as a result they fail to approximate Monte Carlo learning when λ = 1. It may seem that this is inevitable for any online off-policy algorithm; if updates are made on each step on which the target policy is followed, then how could just the right updates be 'un-made' upon deviation from the target policy? In this paper, we introduce a new version of Q(λ) that does exactly that, without significantly increased algorithmic complexity. En route to our new Q(λ), we introduce a new derivation technique based on the forward-view/backward-view analysis familiar from TD(λ) but extended to apply at every time step rather than only at the end of episodes. We apply this technique to derive first a new off-policy version of TD(λ), called PTD(λ), and then our new Q(λ), called PQ(λ).
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".