用元强化学习让电动出租车队适应不断变化的充电网络。
Few-Shot Learning for Dynamic Operations of Automated Electric Taxi Fleets under Evolving Charging Infrastructure: A Meta-Deep Reinforcement Learning Approach
- 结合图注意力网络与概率嵌入，实现动态环境下的快速策略适应。
- 在成都真实数据上测试，对未见过的充电布局泛化能力更强。
- 适合研究智能交通系统或自动驾驶调度的从业者参考。
随着电动汽车（EV）和充电基础设施的快速扩展，自主电动出租车（AET）车队在充电设施动态且不确定的环境中面临管理挑战。现有研究多假设充电网络静态，导致理论模型与实际运行存在显著差距。为此，我们提出GAT-PEARL，一种新颖的元强化学习框架，用于学习自适应运营策略。该方法融合图注意力网络（GAT），有效提取复杂城市环境下基础设施布局的鲁棒空间表征，并建模时空关联；同时采用概率嵌入的演员-评论家强化学习（PEARL），实现无需重训练即可快速适应充电网络布局变化的推理式调整。基于中国成都的真实数据进行大规模仿真验证表明，GAT-PEARL显著优于传统强化学习基线，在未见过的基础设施布局下展现更强泛化能力，并在动态环境中实现更高的整体运营效率。
原文摘要 · Abstract (English)
With the rapid expansion of electric vehicles (EVs) and charging infrastructure, the effective management of Autonomous Electric Taxi (AET) fleets faces a critical challenge in environments with dynamic and uncertain charging availability. While most existing research assumes a static charging network, this simplification creates a significant gap between theoretical models and real-world operations. To bridge this gap, we propose GAT-PEARL, a novel meta-reinforcement learning framework that learns an adaptive operational policy. Our approach integrates a graph attention network (GAT) to effectively extract robust spatial representations under infrastructure layouts and model the complex spatiotemporal relationships of the urban environment, and employs probabilistic embeddings for actor-critic reinforcement learning (PEARL) to enable rapid, inference-based adaptation to changes in charging network layouts without retraining. Through extensive simulations on real-world data in Chengdu, China, we demonstrate that GAT-PEARL significantly outperforms conventional reinforcement learning baselines, showing superior generalization to unseen infrastructure layouts and achieving higher overall operational efficiency in dynamic settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。