A Reinforcement Learning Framework for Autonomous Eco-Driving
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Date
2020-05
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Publisher
The Ohio State University
Abstract
Eco-driving involves adaptively changing the speed of the vehicle to ensure minimal fuel consumption. We pose this problem within the framework of Markov decision problem with discounted reward. The key difficulty lies in identifying the state space and the reward function of the vehicle to be able to use reinforcement learning methods so that the vehicle can learn not only the optimal driving strategy but also the rules of the road through reinforcement learning method. We use deep Q learning and enhanced policy iteration to determine the optimal driving strategy.