Learning incentivization strategy for resource rebalancing in shared services with a budget constraint
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Bibliographic record
Abstract
In this paper, we describe the problem of learning an optimal incentivization strategy that maximizes the service level given a fixed budget constraint for a sharing service such as bike-sharing, carsharing, etc. in a spatiotemporal environment. The service level can be affected due to an imbalance in supply and demand at different locations during a specific time period. We describe and present our study and comparison of various reinforcement learning algorithms on a 1-D problem setting in a simulated bike-share system with a budget constraint on the incentives. We empirically study the performance of three policy gradient based reinforcement learning algorithms, namely: Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and Actor Critic using Kronecker-Factored Trust Region (ACKTR).
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it