An extended ϵ‐constraint method for a multiobjective finite‐horizon Markov decision process
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Bibliographic record
Abstract
Abstract A Markov decision process (MDP) is an appropriate mathematical framework for analysis and modeling a large class of sequential decision‐making problems. Real‐world applications necessitate the evaluation of the value of a decision according to several conflicting objectives. This paper presents an extended ϵ‐constraint method for a multiobjective finite‐horizon MDP. This study integrates the ϵ‐constraint method with the K‐ best policies algorithm to find the nondominated deterministic Markovian policies on the Pareto‐optimal frontier. The proposed algorithm is evaluated on biobjective maintenance scheduling and machine running speed selection problems, and its performance is compared with a classic approach in the literature (weighted‐sum, WS, method). Satisfying results show that the proposed algorithm obtains a good‐quality Pareto frontier and has advantages over the WS method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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