Loading...

Neuromuscular control of sagittal ARM during repetitive movement by actor-critic reinforcement learning method

Golkhou, V ; Sharif University of Technology | 2004

132 Viewed
  1. Type of Document: Article
  2. Publisher: 2004
  3. Abstract:
  4. In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by State-Action-Reward-State-Action (SARSA) method. The system showed excellent tracking capability and after 280 epochs the RMS error for position and velocity profiles were 0.02, 0.04 radian and radian/sec, respectively
  5. Keywords:
  6. Actor-Critic ; CMAC ; Motor control ; Reinforcement learning ; Simulink
  7. Source: Intelligent Automation and Control Trends, Principles, and Applications - International Symposium on Intelligent Automation and Control, ISIAC - Sixth Biannual World Automation Congress, WAC 2004, Seville, 28 June 2004 through 1 July 2004 ; 2004 , Pages 371-376 ; 1889335223 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/1438682