CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation

Abstract

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.

References in corpus (6)

• Semi-Supervised Classification with Graph Convolutional Networks
• Playing Atari with Deep Reinforcement Learning
• The generalization error of random features regression: Precise asymptotics and double descent curve
• DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
• Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
• Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks