Autonomous learning of sequential tasks: experiments and analyses

This paper presents a novel learning model CLARION, which is a hybrid model based on the two-level approach proposed by Sun. The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations, respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and analyzes in various ways are reported that shed light on the advantages of the model.