  Visual navigation in complex environments is inefficient with traditional
reactive policy or general-purposed recurrent policy. To address the long-term
memory issue, this paper proposes a graph attention memory (GAM) architecture
consisting of memory construction module, graph attention module and control
module. The memory construction module builds the topological graph based on
supervised learning by taking the exploration prior. Then, guided attention
features are extracted with the graph attention module. Finally, the deep
reinforcement learning based control module makes decisions based on visual
observations and guided attention features. Detailed convergence analysis of
GAM is presented in this paper. We evaluate GAM-based navigation system in two
complex 3D environments. Experimental results show that the GAM-based
navigation system significantly improves learning efficiency and outperforms
all baselines in average success rate.
