Adaptively Finding Optimal Routes under Principles of Spatial Cognition: A Hierarchical Reinforcement Learning Approach

摘要

Way finding research has paid much attention to the selection of optimal routes under principles of spatial cognition. However, the commonly employed implemental approaches suffer inevitably from the contradictions between personalized network modelling and network data sharing. This paper presents one kind of interactive route selection approach based on hierarchical reinforcement learning. In this approach, a complete network model is unnecessary, but the environmental states are automatically perceived by the agent and then mapped into the reward function defining the goal of cognitively optimal routes. The optimal routes corresponding to the policies with maximal cumulative rewards can be found through a two-stage learning process including a pre-learning stage and a real-time learning one. Our experimental results show that the proposed approach learns effectively enough for real-time route selection and ensures found routes close to global optimal ones.