Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost
Bibliographic record
Abstract
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP)$\mathcal{M}$when we only have access to an approximate model$\hat{\mathcal{M}}$. If we design an optimal policy$\hat{\pi}^{\star}$for the approximate model, how well does it perform when used in the true model$\mathcal{M}$? We provide an answer to this question by bounding a weighted norm of the difference between the value function of$\hat{\pi}^{\star}$when used in$\mathcal{M}$and the optimal value function of$\mathcal{M}$. The use of weighted norm allows us to obtain meaningful bounds for performance loss even when the per-step cost function is unbounded. This is in contrast to much of the prior literature which has largely focused only on the case of bounded per-step cost. We illustrate our results for two specific instances - a finite MDP model for an inventory control problem and the discounted linear quadratic regulator problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".