Deep Reinforcement Learning for Visual Semantic Navigation with Memory

Navigation is an important activity to be performed by mobile robots with high complexity in the context of indoor environments. Approaches as Deep Reinforcement Learning has been adopted for this purpose, from the premise of learning through experiences and taking advantage of Deep Neural Networks as Convolutional Networks, Graph Neural Networks, and Recurrent Networks. Based on the use of vision and semantic context applied in this work, the effects of adding Recurrent Networks on a learning-based navigation model are investigated, making possible the learning of better policies with the use of memory from past experiences. Results obtained show that the proposed approach gets better values in terms of qualitative as quantitative measures when compared to models without memory.