Reinforcement Learning Algorithm with CTRNN in Continuous Action Space

  • Hiroaki Arie
  • Jun Namikawa
  • Tetsuya Ogata
  • Jun Tani
  • Shigeki Sugano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


There are some difficulties in applying traditional reinforcement learning algorithms to motion control tasks of robot. Because most algorithms are concerned with discrete actions and based on the assumption of complete observability of the state. This paper deals with these two problems by combining the reinforcement learning algorithm and CTRNN learning algorithm. We carried out an experiment on the pendulum swing-up task without rotational speed information. It is shown that the information about the rotational speed, which is considered as a hidden state, is estimated and encoded on the activation of a context neuron. As a result, this task is accomplished in several hundred trials using the proposed algorithm.


Discrete Action Reinforcement Learn Algorithm Total Reward Reinforcement Learning Method Complete Observability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hiroaki Arie
    • 1
  • Jun Namikawa
    • 3
  • Tetsuya Ogata
    • 2
  • Jun Tani
    • 3
  • Shigeki Sugano
    • 1
  1. 1.Department of Mechanical EngineeringWaseda UniversityTokyoJapan
  2. 2.Graduate School of InfomaticsKyoto UniversityKyotoJapan
  3. 3.RIKEN, Brain Science InstituteLaboratory for Behavior and Dynamic CognitionSaitamaJapan

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