Dyna-Style Planning with Linear Function Approximation and Prioritized\n Sweeping
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of efficiently learning optimal control policies and\nvalue functions over large state spaces in an online setting in which estimates\nmust be available after each interaction with the world. This paper develops an\nexplicitly model-based approach extending the Dyna architecture to linear\nfunction approximation. Dynastyle planning proceeds by generating imaginary\nexperience from the world model and then applying model-free reinforcement\nlearning algorithms to the imagined state transitions. Our main results are to\nprove that linear Dyna-style planning converges to a unique solution\nindependent of the generating distribution, under natural conditions. In the\npolicy evaluation setting, we prove that the limit point is the least-squares\n(LSTD) solution. An implication of our results is that prioritized-sweeping can\nbe soundly extended to the linear approximation case, backing up to preceding\nfeatures rather than to preceding states. We introduce two versions of\nprioritized sweeping with linear Dyna and briefly illustrate their performance\nempirically on the Mountain Car and Boyan Chain problems.\n
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it