DCOB: Action space for reinforcement learning of high DoF robots

Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawara

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Reinforcement learning (RL) for robot control is an important technology for future robots since it enables us to design a robot's behavior using the reward function. However, RL for high degree-of-freedom robot control is still an open issue. This paper proposes a discrete action space DCOB which is generated from the basis functions (BFs) given to approximate a value function. The remarkable feature is that, by reducing the number of BFs to enable the robot to learn quickly the value function, the size of DCOB is also reduced, which improves the learning speed. In addition, a method WF-DCOB is proposed to enhance the performance, where wire-fitting is utilized to search for continuous actions around each discrete action of DCOB. We apply the proposed methods to motion learning tasks of a simulated humanoid robot and a real spider robot. The experimental results demonstrate outstanding performance.

Original languageEnglish
Pages (from-to)327-346
Number of pages20
JournalAutonomous Robots
Issue number4
Publication statusPublished - 2013 May
Externally publishedYes


  • Action space
  • Crawling
  • Humanoid robot
  • Motion learning
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence


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