# Online Learning of Energy Consumption for Navigation of Electric Vehicles

arXiv: https://arxiv.org/abs/2111.02314 · HTML: https://mlsanity.com/paper/2111.02314/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [(More) Efficient Reinforcement Learning via Posterior Sampling](https://mlsanity.com/paper/1306.0940/) (2013)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [Why is Posterior Sampling Better than Optimism for Reinforcement Learning?](https://mlsanity.com/paper/1607.00215/) (2016)
- [Batched Multi-armed Bandits Problem](https://mlsanity.com/paper/1904.01763/) (2019)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Sequential Batch Learning in Finite-Action Linear Contextual Bandits](https://mlsanity.com/paper/2004.06321/) (2020)
- [Thompson Sampling for Complex Bandit Problems](https://mlsanity.com/paper/1311.0466/) (2013)
- [Efficient Learning in Large-Scale Combinatorial Semi-Bandits](https://mlsanity.com/paper/1406.7443/) (2014)
- [Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits](https://mlsanity.com/paper/2008.11918/) (2020)
- [Prior-free and prior-dependent regret bounds for Thompson Sampling](https://mlsanity.com/paper/1304.5758/) (2013)
- [On Bayesian index policies for sequential resource allocation](https://mlsanity.com/paper/1601.01190/) (2016)
- [Online Learning of Network Bottlenecks via Minimax Paths](https://mlsanity.com/paper/2109.08467/) (2021)
- [Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting](https://mlsanity.com/paper/1703.05452/) (2017)
- [An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles](https://mlsanity.com/paper/2003.01416/) (2020)
- [Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits](https://mlsanity.com/paper/1307.6134/) (2013)
- [Stochastic Online Shortest Path Routing: The Value of Feedback](https://mlsanity.com/paper/1309.7367/) (2013)

## Built on by (most cited first, 7 of 7)

- [Tree Ensembles for Contextual Bandits](https://mlsanity.com/paper/2402.06963/) (2024)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Bayesian Analysis of Combinatorial Gaussian Process Bandits](https://mlsanity.com/paper/2312.12676/) (2023)
- [Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling](https://mlsanity.com/paper/2502.01226/) (2025)
- [Passive and Active Learning of Driver Behavior from Electric Vehicles](https://mlsanity.com/paper/2203.02179/) (2022)
- [A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles](https://mlsanity.com/paper/2301.07156/) (2023)
- [Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs](https://mlsanity.com/paper/2303.15087/) (2023)

## Abstract (arXiv)

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.