Average Reward Reinforcement Learning for Optimal On-route Charging of Electric Buses
Bibliographic record
Abstract
Nowadays, due to the severe environmental concerns caused by the emissions from the conventional transportations which use traditional fossil sources, people want to find an alternative method imperatively. Under this condition, electric buses attract transit service providers' attention. However, since the degradation of the battery and charging load for the local grid, the charging schedule is a critical issue to be addressed. In this paper, in order to obtain the optimal on-route charging schedule, a specific physical model and battery degradation model is built for the calculation of the energy consumption and the cost. Semi-Markov decision process (SMDP) is utilized to simulate the running process of the EBs, and the average reward reinforcement learning (ARRL) is introduced to optimize the on-route charging schedule for the EBs. The charging policy and the performance is compared with the default charging schedule according to the real EB operation data provided by the St. Albert Transit, AB, Canada.
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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 | 0.000 | 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 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".