Simulation-based generation of heuristics for decision-making in stochastic environments
Bibliographic record
Abstract
Decision-making in stochastic environments often requires a trade-off between performance and interpretability. Although Reinforcement Learning (RL) excels at creating adaptive policies, the resulting solutions are not transparent. Conversely, while heuristics offer transparency, they often lack optimality and adaptability. In this work, we present a general framework that combines the strengths of both approaches. First, we use RL to train a policy on a Markov Decision Process (MDP). Then, we extract transparent heuristics in the form of decision trees via interpretable learning (VIPER). To conclude our method, we apply pruning to the tree, aiming to simplify its structure and improve the generalisation of the resulting rule set. We demonstrate this approach using a logistics case study involving significant variability in production and demand. The resulting heuristics outperform expert-designed rules and match the performance of the original RL policy, offering transparency and robustness. This method allows for data-driven, explainable decision-making that does not require domain-specific expertise.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".