PAC Reinforcement Learning With an Imperfect Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinforcement learning (RL) methods have proved to be successful in many simulated environments. The common approaches, however, are often too sample intensive to be applied directly in the real world. A promising approach to addressing this issue is to train an RL agent in a simulator and transfer the solution to the real environment. When a high-fidelity simulator is available we would expect significant reduction in the amount of real trajectories needed for learning. In this work we aim at better understanding the theoretical nature of this approach. We start with a perhaps surprising result that, even if the approximate model (e.g., a simulator) only differs from the real environment in a single state-action pair (but which one is unknown), such a model could be information-theoretically useless and the sample complexity (in terms of real trajectories) still scales with the total number of states in the worst case. We investigate the hard instances and come up with natural conditions that avoid the pathological situations. We then propose two conceptually simple algorithms that enjoy polynomial sample complexity guarantees with no dependence on the size of the state-action space, and prove some foundational results to provide insights into this important problem.
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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) | 0.000 | 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 | 0.002 | 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 it