Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

Carl Johan Hoel, Katherine Driggs-Campbell, Krister Wolff, Leo Laine, Mykel J. Kochenderfer

Research output: Contribution to journalArticlepeer-review

Abstract

Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.

Original languageEnglish (US)
Article number8911507
Pages (from-to)294-305
Number of pages12
JournalIEEE Transactions on Intelligent Vehicles
Volume5
Issue number2
DOIs
StatePublished - Jun 2020

Keywords

  • Autonomous driving
  • Monte Carlo tree search
  • reinforcement learning
  • tactical decision making

ASJC Scopus subject areas

  • Artificial Intelligence
  • Automotive Engineering
  • Control and Optimization

Fingerprint Dive into the research topics of 'Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving'. Together they form a unique fingerprint.

Cite this