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A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation

A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation

Jean-Michel Ilié, Ahmed-Chawki Chaouche, François Pêcheux
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 19
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.300792
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MLA

Ilié, Jean-Michel, et al. "A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation." IJACI vol.13, no.1 2022: pp.1-19. http://doi.org/10.4018/IJACI.300792

APA

Ilié, J., Chaouche, A., & Pêcheux, F. (2022). A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-19. http://doi.org/10.4018/IJACI.300792

Chicago

Ilié, Jean-Michel, Ahmed-Chawki Chaouche, and François Pêcheux. "A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-19. http://doi.org/10.4018/IJACI.300792

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Abstract

Due to contextual traffic conditions, the computation of optimized or shortest paths is a very complex problem for both drivers and autonomous vehicles. This paper introduces a reinforcement learning mechanism that is able to efficiently evaluate path durations based on an abstraction of the available traffic information. The authors demonstrate that a cache data structure allows a permanent access to the results whereas a lazy politics taking new data into account is used to increase the viability of those results. As a client of the proposed learning system, the authors consider a contextual path planning application and they show in addition the benefit of integrating a client cache at this level. Our measures highlight the performance of each mechanism, according to different learning and caching strategies.

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