Framework for control and deep reinforcement learning in traffic

Cathy Wu, Kanaad Parvate, Nishant Kheterpal, Leah DIckstein, Ankur Mehta, Eugene Vinitsky, Alexandre M. Bayen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in deep reinforcement learning (RL) offer an opportunity to revisit complex traffic control problems at the level of vehicle dynamics, with the aim of learning locally optimal policies (with respect to the policy parameterization) for a variety of objectives such as matching a target velocity or minimizing fuel consumption. In this article, we present a framework called CISTAR (Customized Interface for SUMO, TraCI, and RLLab) that integrates the widely used traffic simulator SUMO with a standard deep reinforcement learning library RLLab. We create an interface allowing for easy customization of SUMO, allowing users to easily implement new controllers, heterogeneous experiments, and user-defined cost functions that depend on arbitrary state variables. We demonstrate the usage of CISTAR with several benchmark control and RL examples.

Original languageEnglish (US)
Title of host publication2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781538615256
DOIs
StatePublished - Jul 2 2017
Event20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Duration: Oct 16 2017Oct 19 2017

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-March

Other

Other20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Country/TerritoryJapan
CityYokohama, Kanagawa
Period10/16/1710/19/17

Keywords

  • control
  • deep reinforcement learning
  • Simulation
  • vehicle dynamics

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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