Autonomous agent response learning by a multi-species particle swarm optimization

An autonomous agent response learning (AARL) algorithm is presented in this paper. We proposed to decompose the award function into a set of local award functions. By optimizing this objective function set, the response function with maximum award can be determined. To tackle the optimization problem, a modified particle swarm optimization (PSO) called "multi-species PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. Two sets of experiments are provided to illustrate the performance of MS-PSO. The results show that it returns a more accurate response set within shorter duration by comparing with other PSO methods.

[1]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  Christopher G. Atkeson,et al.  Learning from observation using primitives , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  Tomohiro Yamaguchi,et al.  Realtime reinforcement learning for a real robot in the real environment , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  Katsushi Ikeuchi,et al.  Modeling manipulation interactions by hidden Markov models , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Takeshi Ohashi,et al.  Stochastic field model for autonomous robot learning , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[7]  Leslie Pack Kaelbling,et al.  On reinforcement learning for robots , 1996, IROS.

[8]  Timothy Gordon,et al.  Associative reinforcement learning for discrete-time optimal control , 2000 .

[9]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  Christopher G. Atkeson,et al.  Using locally weighted regression for robot learning , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[11]  H.-M. Gross,et al.  A neural field approach to topological reinforcement learning in continuous action spaces , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[12]  S. Kaitwanidvilai,et al.  Active Bayesian feature weighting in reinforcement learning robot , 2002, 2002 IEEE International Conference on Industrial Technology, 2002. IEEE ICIT '02..

[13]  Stefan Schaal,et al.  Robot learning by nonparametric regression , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).