Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to the multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance in the single-agent and multi-agent settings, through an analysis of the algorithm under batched feedback. Finally, we demonstrate the performance of our methods via experiments on several real-world city road networks.

翻译：高效能耗导航是电动汽车面临的重要挑战，因其电池容量有限。我们采用贝叶斯方法对道路段的能耗进行建模，以实现高效导航。为学习模型参数，我们开发了一个在线学习框架，并研究了多种探索策略，如汤普森采样（Thompson Sampling）和置信上界（Upper Confidence Bound）。随后，我们将在线学习框架扩展到多智能体场景，使多辆车能够自适应地导航并学习能耗模型参数。通过分析批处理反馈下的算法，我们评估了汤普森采样在单智能体和多智能体场景中的表现，并建立了严格的遗憾界。最后，通过在多个真实城市道路网络上的实验，验证了所提出方法的性能。