Gradual learning for behavior acquisition by evolving artificial neural network

Abstract

This paper describes a behavior acquisition of virtual robots by evolving artificial neural network (EANN) with a gradual learning. The gradual learning is a method in which initial states of simulation for evaluation is changing as optimization progresses. Motion of virtual robot is calculated by the physical engine PhysX, and it is controlled by an ANN. Parameters of an ANN are optimized by particle swarm optimization (PSO) so that a virtual robot follows the given target. Experimental results show that the gradual learning is better than a common learning method, realizing the standing behaviors which are not acquired by a common learning at all. It is also shown that random initialization of solutions in the middle of optimization leads to better behaviors.