Accelerating Learning of Route Choices With C2I: A Preliminary Investigation

  • Guilherme Santos Universidade Federal do Rio Grande do Sul
  • Ana Bazzan Universidade Federal do Rio Grande do Sul


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.

Palavras-chave: Multi-Agent Reinforcement Learning, Route Choice, Car-to-Infrastructure Communication


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SANTOS, Guilherme; BAZZAN, Ana. Accelerating Learning of Route Choices With C2I: A Preliminary Investigation. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 41-48. ISSN 2763-8944. DOI: