Autoregressive Dynamics Models for Offline Policy Evaluation and\n Optimization
Bibliographic record
Abstract
Standard dynamics models for continuous control make use of feedforward\ncomputation to predict the conditional distribution of next state and reward\ngiven current state and action using a multivariate Gaussian with a diagonal\ncovariance structure. This modeling choice assumes that different dimensions of\nthe next state and reward are conditionally independent given the current state\nand action and may be driven by the fact that fully observable physics-based\nsimulation environments entail deterministic transition dynamics. In this\npaper, we challenge this conditional independence assumption and propose a\nfamily of expressive autoregressive dynamics models that generate different\ndimensions of the next state and reward sequentially conditioned on previous\ndimensions. We demonstrate that autoregressive dynamics models indeed\noutperform standard feedforward models in log-likelihood on heldout\ntransitions. Furthermore, we compare different model-based and model-free\noff-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo\ndatasets, and find that autoregressive dynamics models consistently outperform\nall baselines, achieving a new state-of-the-art. Finally, we show that\nautoregressive dynamics models are useful for offline policy optimization by\nserving as a way to enrich the replay buffer through data augmentation and\nimproving performance using model-based planning.\n
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".