Learning machine that uses context structure to search policy

Abstract

A system that transits from the initial state to the target state is assumed. The process of state transition is represented by time series data. The time series data is not given to the system unlike a program of a computer, but acquired by trial and error. To combine and search time series data, the context structure inherent in time series data is used. For example, even if the details of the time series data leading to the target state at the time of searching can not be determined, the time series data immediately before reaching the target state and the time series data indicating the movement from the initial state are linked at the upper level of the context In other words, if there is an overlap in the tree structure, it becomes a search candidate. It has been announced that the hierarchical structure is inherent in the time series data and that the basic sequence making up the time series data can naturally correspond to the activation area in the neural network.