Combining simulation and reinforcement learning to reduce food waste in food retail
Bibliographic record
Abstract
Extraordinary amounts of fresh produce are never purchased and are discarded as waste. Reinforcement learning (RL) could serve as a means to improve business profits while reducing food waste via control of store pricing and ordering decisions. We present a discrete-event-based simulation framework for food retail which simulates wholesaler, store, and customer interactions. This simulator is critical for driving development and testing of future RL methods. It provides an efficient learning feedback system across a wide gamut of possible scenarios, which cannot be replicated from live observations or pure historical data alone. This is crucial as RL agents cannot learn robust decision-making policies without exposure to many unique scenarios. We evaluate our simulator on a demonstrative case generated from historical consumption and price data using a provided methodology for synthesizing daily demand from monthly and yearly stats. In this demonstrative case, we investigate proximal policy optimization, soft actor–critic, and deep Q networks trained with different reward formulations to decrease food waste and improve profits. These RL methods reduced food waste by 78%–92% on average on an unseen 3-year test period as compared to a baseline mimicking typical food retail waste. Compared to a second popular baseline in literature, the best performing RL algorithm was able to improve profits by up to 12.3%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".