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Path-Integral-Based Reinforcement Learning Algorithm for Goal-Directed Locomotion of Snake-Shaped Robot

Author

Listed:
  • Qi Yongqiang
  • Yang Hailan
  • Rong Dan
  • Ke Yi
  • Lu Dongchen
  • Li chunyang
  • Liu Xiaoting
  • A. E. Matouk

Abstract

This paper proposes a goal-directed locomotion method for a snake-shaped robot in 3D complex environment based on path-integral reinforcement learning. This method uses a model-free online Q-learning algorithm to evaluate action strategies and optimize decision-making through repeated “exploration-learning-utilization†processes to complete snake-shaped robot goal-directed locomotion in 3D complex environment. The proper locomotion control parameters such as joint angles and screw-drive velocities can be learned by path-integral reinforcement learning, and the learned parameters were successfully transferred to the snake-shaped robot. Simulation results show that the planned path can avoid all obstacles and reach the destination smoothly and swiftly.

Suggested Citation

  • Qi Yongqiang & Yang Hailan & Rong Dan & Ke Yi & Lu Dongchen & Li chunyang & Liu Xiaoting & A. E. Matouk, 2021. "Path-Integral-Based Reinforcement Learning Algorithm for Goal-Directed Locomotion of Snake-Shaped Robot," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-12, February.
    • Handle: RePEc:hin:jnddns:8824377
    DOI: 10.1155/2021/8824377
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