Stochastic Learning Cellular Automata
Fei Qian, Hironori Hirata · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · 1995
In an artificial neural network, the behavior modification is accomplished by parameter adjustment. Often, the adjustable parameters in the neural networks are called synaptic weights or connection strengths, but their analogy to real biology reinforcement learning to update has been on pattern classification and simple time-delayed feedback control tasks. There has been no significant work to use reinforcement learning techniques to carry out structural learning. To construct the model of reinforcement learning systems, in this article, we generalize the stochastic cellular automata to stochastic learning cellular automata, which is a combined model of traditional stochastic cellular automata and random environments, and give some definitions of stochastic learning cellular automaton.