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 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:节能导航是电动车辆面临的一项重大挑战,因为其电池容量有限。我们采用贝叶斯式的方法模拟公路段的能源消耗,以便高效导航。为了学习模型参数,我们开发了一个在线学习框架,并调查几个探索战略,如Thompson抽样和超信任库。然后,我们将我们的在线学习框架扩大到多试剂环境,使多车辆适应性导航并学习能源模型的参数。我们分析Thompson抽样,并通过对分批反馈的算法进行分析,对其在单一试剂和多试剂环境下的性能建立严格的遗憾界限。最后,我们通过几个真实世界城市道路网络的实验,展示了我们方法的绩效。