A Reinforcement Learning Integrating Distributed Caches for Contextual Road Navigation

Jean-Michel Ilié, Ahmed-Chawki Chaouche and François Pêcheux
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Jean-Michel Ilié: Sorbonne University, France
Ahmed-Chawki Chaouche: University of Constantine 2, Algeria
François Pêcheux: Sorbonne University, France

International Journal of Ambient Computing and Intelligence (IJACI), 2022, vol. 13, issue 1, 1-19

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.

Date: 2022
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