A Deep Reinforcement Learning Approach to Marginalized Importance\n Sampling with the Successor Representation
Bibliographic record
Abstract
Marginalized importance sampling (MIS), which measures the density ratio\nbetween the state-action occupancy of a target policy and that of a sampling\ndistribution, is a promising approach for off-policy evaluation. However,\ncurrent state-of-the-art MIS methods rely on complex optimization tricks and\nsucceed mostly on simple toy problems. We bridge the gap between MIS and deep\nreinforcement learning by observing that the density ratio can be computed from\nthe successor representation of the target policy. The successor representation\ncan be trained through deep reinforcement learning methodology and decouples\nthe reward optimization from the dynamics of the environment, making the\nresulting algorithm stable and applicable to high-dimensional domains. We\nevaluate the empirical performance of our approach on a variety of challenging\nAtari and MuJoCo environments.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| 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".