Operant conditioning learning model based on BP network
Jing Huang, Xiaogang Ruan, Li Lei, Ruoyan Wei, Qingwu Fan, Xuan Wu · 2014
The naissance of cognitive robotics marks that psychology is more and more highly involved in the artificial intelligence research. Inspired by psychology and ethology, we propose an operant conditioning learning model based on BP (back-propagation) network named OCLMBP on the basis of Skinner's relevant theory. The model is applied to the problem of obstacle avoidance with a wheeled robot. The robot controlled by the model can learn to avoid obstacles through a learning-by-doing style without any external supervision, but by the proximity sensors information as positive or negative reinforcement signals. The results are compared with original OCLM (operant conditioning learning model), and the proposed model has better performance.