Bibliographic record
Abstract
In planning with partially observable Markov decision processes, pre-compiled policies are often represented as finite state controllers or sets of alpha-vectors, which provide a lower bound on the value of the optimal policy. Some algorithms (e.g., HSVI2, SARSOP, GapMin) also compute an upper bound to guide the search and to offer performance guarantees, but they do not derive a policy from this upper bound due to computational reasons. The execution of a policy derived from an upper bound requires a one step lookahead simulation to determine the next best action and the evaluation of the upper bound at the reachable beliefs is complicated and costly (i.e., linear programming or sawtoooth approximation). The first aim of this paper is to show principled and computationally cheap ways of executing upper bound policies which can be even faster than executing lower bound policies based on alpha vectors. The second complementary contribution is a new method to find better upper bound policies that outperforms those obtained by existing algorithms, such as HSVI2, SARSOP, or GapMin, on a suite of benchmarks. Our approach is based on a novel synthesis of augmented and deterministic POMDPs and it facilitates efficient optimization of upper bound policies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".