Research on Path-Planning of Manipulator Based on Multi-Agent Reinforcement Learning

Because of the dynamic characteristic of high nonlinear,strong coupling and variable structure,it is difficult to perform effective controlling on the robot manipulator by conventional controlling theory.In this paper,a new approach of multi-agent reinforcement learning method based on Kohonen net is proposed which is used in the multi-agent environment of robot manipulator path-planning and the simulation experiment shows the validity of this method.

[1]  Helge J. Ritter,et al.  Neural computation and self-organizing maps - an introduction , 1992, Computation and neural systems series.

[2]  Angel P. del Pobil,et al.  Practical Motion Planning in Robotics: Current Approaches and Future Directions , 1998 .

[3]  Liang Tong,et al.  A Speedup Convergent Method for Multi-Agent Reinforcement Learning , 2009, 2009 International Conference on Information Engineering and Computer Science.

[4]  Ashwin Ram,et al.  Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces , 1997, Adapt. Behav..

[5]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[6]  Heinz Wörn,et al.  6 DOF path planning in dynamic environments-a parallel online approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[7]  Yong K. Hwang,et al.  SANDROS: a dynamic graph search algorithm for motion planning , 1998, IEEE Trans. Robotics Autom..

[8]  M.A. Wiering,et al.  Reinforcement Learning in Continuous Action Spaces , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

[9]  Narendra Ahuja,et al.  Gross motion planning—a survey , 1992, CSUR.