Hyperparameter Tuning in Offline Reinforcement Learning
Bibliographic record
Abstract
In this work, we propose a reliable hyperparameter tuning scheme for offline reinforcement learning. We demonstrate our proposed scheme using the simplest antmaze environment from the standard benchmark offline dataset, D4RL. The usual approach for policy evaluation in offline reinforcement learning involves online evaluation, i.e., cherry-picking best performance on the test environment. To mitigate this cherry-picking, we propose an ad-hoc online evaluation metric, which we name "median-median-return". This metric enables more reliable reporting of results because it represents the expected performance of the learned policy by taking the median online evaluation performance across both epochs and training runs. To demonstrate our scheme, we employ the recently state-of-the-art algorithm, IQL, and perform a thorough hyperparameter search based on our proposed metric. The tuned architectures enjoy notably stronger cherry-picked performance, and the best models are able to surpass the reported state-of-the-art performance on average.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".