Hierarchical Deep Reinforcement Learning for Charging Scheduling of Electric Buses with Uncertainties
Bibliographic record
Abstract
The large-scale popularization of electric buses (EBs) marks a significant stride in sustainable development strategies aimed at environmental conservation. A critical concern for bus companies is reducing the operational costs associated with charging these vehicles. This task is particularly challenging due to the uncertainties in travel time, energy consumption, and fluctuating electricity prices, compounded by the constraints of limited charging infrastructure. This paper tackles these complexities by leveraging Deep Reinforcement Learning (DRL), which excels in learning directly from environmental interactions without relying on pre-defined models. We conceive two augmented Markov Decision Processes (MDPs) and propose a novel Hierarchical Deep Reinforcement Learning (HDRL) algorithm called Double Actor-Critic Multi-Agent Proximal Policy Optimization (DAC-MAPPO). The proposed algorithm enhances learning efficiency and convergence speed by integrating the MAPPO algorithm into the DAC architecture. Specifically, a centralized high-level agent is responsible for making charger allocation decisions, while multiple decentralized low-level agents determine the charging power for each EB at every time step. Experimental evaluations using real-world data demonstrate the superior performance and effectiveness of the DAC-MAPPO algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".