Multi-Agent Learning for Post-Trained Autonomous Driving Decision Making
Keywords:
multi-agent learning, post-training, autonomous driving, decision making, socio-technical systems, safety, governance, sustainabilityAbstract
Autonomous driving decision making is increasingly framed as a socio-technical problem in which perception, prediction, planning, and control must operate under uncertainty, regulatory scrutiny, and heterogeneous human expectations. This paper examines multi-agent learning for post-trained autonomous driving decision making, treating post-training as the stage in which pretrained representation models are adapted toward instruction following, preference alignment, safety constraints, and interactive coordination. The central argument is that multi-agent learning is not merely a technical extension of single-vehicle reinforcement learning but a system-level architecture that redistributes intelligence across vehicles, roadside infrastructure, fleet operations, simulation environments, and governance institutions. Such redistribution creates important trade-offs involving latency, robustness, interpretability, fairness, energy consumption, liability, and long-term maintainability. The paper synthesizes foundational work on multi-agent reinforcement learning, end-to-end driving, imitation learning, and post-training alignment, then discusses deployment architectures that combine centralized training with decentralized execution, mixed cooperative-competitive interaction, and human-in-the-loop oversight. It also considers validation, edge infrastructure, sustainability, and policy implications. The analysis suggests that post-trained multi-agent driving systems can improve adaptability and coordination, but only when their value conflicts, failure modes, and accountability pathways are designed explicitly rather than treated as downstream engineering concerns. The conclusion outlines research directions for safe, equitable, and sustainable deployment.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
