Maximizing the probability of arriving on time: A practical q-learning method


Cao, Z, Guo, H, Zhang, J, Oliehoek, F and Fastenrath, U (2017) Maximizing the probability of arriving on time: A practical q-learning method. In: UNSPECIFIED, ? - ?.

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Abstract

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Depositing User: Symplectic Admin
Date Deposited: 10 Jan 2017 11:40
Last Modified: 08 Aug 2018 07:10
URI: http://livrepository.liverpool.ac.uk/id/eprint/3005096

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