CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation
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Abstract (arXiv)
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency. Deciding which stations to recommend drivers to is a complex problem with a multitude of possible recommendations, volatile usage patterns and temporally extended consequences of recommendations. Reinforcement learning offers a powerful paradigm for solving sequential decision-making problems, but traditional methods may struggle with sample efficiency due to the high number of possible actions. By developing a model that allows complex representations of actions, we improve outcomes for users of our system by over 30% when compared to existing baselines in a simulation. If implemented widely, these better recommendations can globally save over 4 million person-hours of waiting and driving each year.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 28 Jan 2021.
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References · 10
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2019The generalization error of random features regression: Precise asymptotics and double descent curvecited by 410
- 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 114
- 2019DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learningcited by 54
- 2019Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matchingcited by 26
- 2018MOVI: A Model-Free Approach to Dynamic Fleet Managementcited by 23
- 2018Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Miningcited by 8
- 2018Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networkscited by 1
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