Beyond Adaptive Critic - Creative Learning for Intelligent Autonomous Mobile Robots

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. In the adaptive critic family, globalized dual heuristic programming (GDHP) is actually combined heuristic dynamic programming (HDP) and dual heuristic programming (DHP) based on dynamic programming (DP). The objective of this paper is to explore more generalized methods for the adaptive critic family. It is beyond adaptive critic learning theory and defined as creative learning (CL). Creative learning includes all the components in the adaptive critic family, which is to generalize GDHP by modifying the learning rates and utilizing multiple criteria (or critic) and increasing the degree of derivatives of the J (critic) function. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The significance of this paper is to better understand the adaptive critic learning theory and move forward to develop more human-intelligence-like components into the intelligent robot controller. Moreover, it should extend to other applications. Eventually, integrating a criteria knowledge database into the action module will develop a real imagination adaptive critic learning module.