A SIMULATION ENVIRONMENT FOR REDUCING FOOD WASTE VIA REINFORCEMENT LEARNING
Bibliographic record
Abstract
Food retailers discard astonishing amounts of spoiled produce as waste. Reinforcement learning could greatly improve retail store product ordering and pricing decisions to simultaneously reduce food waste and improve business profits. We present a discrete-event-based food retail simulation framework which simulates wholesaler, store, and customer interactions. This simulator is essential for driving development and testing of future reinforcement learning methods to help economically reduce food waste for food retail stores. Simulation provides an efficient learning feedback system across a massive number of possible scenarios, which cannot be replicated from live observation or pure historical data alone. We demonstrate our simulator on an example built from historical consumption and price data. A simple realistic baseline resulted in more than US$1M of food waste (2010-2015). A soft actor critic (SAC) RL agent increased profit by 42% and reduced food waste by almost US$500k over three years (2012-2015) after learning from simulations (2010-2012).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".