Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning
Bibliographic record
Abstract
Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.
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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".