A reinforcement learning system with chaotic neural networks-based adaptive hierarchical memory structure for autonomous robots

Human learns incidents by own actions and reflects them on the subsequent action as own experiences. These experiences are memorized in his brain and recollected if necessary. This research incorporates such an intelligent information processing mechanism, and applies it to an autonomous agent that has three main functions: learning, memorization and associative recollection. In the proposed system, an actor-critic type reinforcement learning method is used for learning. Auto-associative chaotic neural network is also used like mutual associative memory system. Moreover, the memory part has an adaptive hierarchical layered structure of the memory module that consists of chaotic neural networks in consideration of the adjustment to non-MDP (Markov Decision Process) environment. Finally, the effectiveness of this proposed method is verified through the simulation applied to the maze-searching problem.