Highway Environment Model for Reinforcement Learning

Tamás Bécsi, Szilárd Aradi, Árpád Fehér, János Szalay, Péter Gáspár

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The paper presents a microscopic highway simulation model, built as an environment for the development of different machine learning based autonomous vehicle controllers. The environment is based on the popular OpenAI Gym framework, hence it can be easily integrated into multiple projects. The traffic flow is operated by classic microscopic models, while the agent's vehicle uses a rigid kinematic single-track model, with either continuous or discrete action spaces. The environment also provides a simple high-level sensor model, where the state of the agent and its surroundings are part of the observation. To aid the learning process, multiple reward functions are also provided.

Original languageEnglish
Pages (from-to)429-434
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number22
DOIs
Publication statusPublished - 2018

Keywords

  • Autonomous Vehicles
  • Machine Learning
  • Reinforcement Learning Control
  • Road traffic

ASJC Scopus subject areas

  • Control and Systems Engineering

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