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Controller Learning Method of Self-driving Bicycle Using State-of-the-art Deep Reinforcement Learning Algorithms

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(10), pp.23-31
  • DOI : 10.9708/jksci.2018.23.10.023
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 17, 2018
  • Accepted : October 1, 2018
  • Published : October 31, 2018

Seungyoon Choi 1 Tuyen Le Pham 1 CHUNG TAECHOONG 1

1경희대학교

Accredited

ABSTRACT

Recently, there have been many studies on machine learning. Among them, studies on reinforcement learning are actively worked. In this study, we propose a controller to control bicycle using DDPG (Deep Deterministic Policy Gradient) algorithm which is the latest deep reinforcement learning method. In this paper, we redefine the compensation function of bicycle dynamics and neural network to learn agents. When using the proposed method for data learning and control, it is possible to perform the function of not allowing the bicycle to fall over and reach the further given destination unlike the existing method. For the performance evaluation, we have experimented that the proposed algorithm works in various environments such as fixed speed, random, target point, and not determined. Finally, as a result, it is confirmed that the proposed algorithm shows better performance than the conventional neural network algorithms NAF and PPO.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.