Open AccessAccelerating Learning of Route Choices With C2I: A Preliminary Investigation
Author(s) -
Santos Guilherme,
Ana L. C. Bazzan
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/kdmile.2020.11957
Subject(s) - reinforcement learning , computer science , process (computing) , order (exchange) , iterative and incremental development , artificial intelligence , business , software engineering , finance , operating system
How to choose a route that takes you from A to B? This is an issue that is turning more and more important in modern societies. One way to address this agenda is through the use of communication between the infrastructure (network), and the demand (vehicles). In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users (agents) can accelerate their learning process regarding route choice problem, via reinforcement learning (RL). We employ a microscopic simulator in order to compare our method with two others: RL without communication and an iterative method. Experimental results show that our method outperforms both methods in terms of effectiveness and efficiency.