Multi-Agent Tsallis Actor–Critic for Autonomous Vehicle Fleet Coordination on Road Graph Networks
Why this work is in the frame
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Bibliographic record
Abstract
We introduce the multi-vehicle road graph delivery problem, aimed at solving real-time delivery tasks using autonomous vehicle fleets. Our approach utilizes road graph representations to accurately capture the characteristics of urban road networks. To address this problem efficiently, we propose a multi-agent reinforcement learning (MARL) framework incorporating an attention-based state encoder, which effectively encodes the road network structure and package information. Our modified implementation of a multi-agent Tsallis actor-critic (MATAC) algorithm, combined with the state encoder, is trained to collaboratively minimize delivery makespan using individualized rewards that encourage cooperative vehicle routing behaviors. Experimental results on multiple benchmark maps demonstrate that our algorithm significantly reduces the makespan more than heuristic approaches, while achieving inference times much faster than an exact optimization method with competitive solution quality. These results highlight the applicability of our method for large-scale real-time delivery scenarios involving autonomous vehicles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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