Self-learning Algorithm for Robot Based on Boltzamnn Machine

In view of the self-balancing movement control problem of the two-wheeled robot,a bionic self-learning algorithm of the robot is proposed as a study mechanism of Skinner's operant conditioning reflection based on the Boltzamnn machine.This algorithm uses the Metropolis criterion in Boltzamnn machine to balance in the proportion of the exploration and the exploitation in the study of Skinner's operant conditioning reflection,and chooses the most superior behavior through certain probability depending on the probability tropism mechanism.Thus the robot can obtain the skill of bionic self-learning like the human or the animal under the unknown environment,and realize the self-balancing movement control of the robot.Finally,the simulation experiments were conducted and the Skinner's operant conditioning reflection study algorithms based on the Boltzamnn machine and the greedy strategy were compared,separately.Results show that the Skinner's operant conditioning reflection study algorithm based on the Boltzamnn machine can obtain the stronger movement balancing control skill and the better dynamic performance,and manifest the self-learning characteristics of the robot.