Conservative In-Distribution Q-Learning for Offline Reinforcement Learning
Bibliographic record
Abstract
Offline Reinforcement Learning (RL) aims to learn policies from pre-collected datasets without any additional interaction. In order to perform well and robustly in dynamic environments with noise or disturbances, the learned value function and derived policy should generalize well within and near the dataset distribution, rather than ‘over-fitting’ to training samples. To meet this requirement, we propose a new approach called Conservative In-Distribution Q-learning (CIDQL) that takes a step towards in-distribution offline RL. CIDQL is designed to learn in-distribution with respect to the dataset, using a perturbation-based interpolation technique and a quantile method for value regularization. It prohibits bootstrapping during value iteration, ensuring stable Q-value learning that is separated from policy improvement. The approach has theoretical guarantees for both Q-value underestimation and non-underestimation, and outperforms most SOTA algorithms on D4RL gym-MuJoCo benchmarks.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".