Route optimization for autonomous bulldozer by distributed deep reinforcement learning

Yasuhiro Osaka, Naoya Odajima, Yutaka Uchimura

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

Abstract

Since the publication showed DQN based reinforcement learning methods exceeds human's score in Atari 2600 video games, various deep reinforcement learning have bee researched. This paper proposes a method to control bulldozer autonomously by learning the sediment leveling route using PPO that enables distributed deep reinforcement learning. The simulator was originally developed that enables to reproduce the behavior of small and uniform sediment. By incorporating an LSTM that processes the input state as time-series data into the agent network, more than 95% of the sediment in the target area on average was achieved. In addition, the generalization performance for unknown condition was evaluated, by giving unlearned conditions were given as initial setups.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Mechatronics, ICM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728144429
DOIs
Publication statusPublished - 2021 Mar 7
Event2021 IEEE International Conference on Mechatronics, ICM 2021 - Kashiwa, Japan
Duration: 2021 Mar 72021 Mar 9

Publication series

Name2021 IEEE International Conference on Mechatronics, ICM 2021

Conference

Conference2021 IEEE International Conference on Mechatronics, ICM 2021
CountryJapan
CityKashiwa
Period21/3/721/3/9

Keywords

  • Artificial intelligence
  • Deep Reinforcement Learning
  • Machine Automation
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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