A hybrid model for learning sequential navigation

To deal with reactive sequential decision tasks, we present a learning model CLARION, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform online, bottom-up learning. Experiments in various situations are reported that shed light on the working of the model