Bionic Robot Control based on Advanced Updating

In view of the balance control problem of self-balance robot, the reinforcement learning mechanism based on the advanced updating is proposed as a self-balance bionic learning algorithm of the robot. This algorithm can choose optimal behavior above the baseline with the certain probability by using baseline in the advanced updating and unifying the probability curiosity mechanism in reinforcement learning, so that the robot can obtain the bionic self-learning skills like creature under the unknown environment, and realize the bionic self-balance controls of the robot. Finally, the simulation experiment is made. The result indicates that the robot can obtain the stronger self-learning skills about the balance control and the better dynamic performance from this learning algorithm of the reinforcement learning based on the advanced updating and it manifests the bionic characteristic about the advanced updating, and gains the higher research and the application value.