  Deep reinforcement learning (RL) has been successfully applied to a variety
of game-like environments. However, the application of deep RL to visual
navigation with realistic environments is a challenging task. We propose a
novel learning architecture capable of navigating an agent, e.g. a mobile
robot, to a target given by an image. To achieve this, we have extended the
batched A2C algorithm with auxiliary tasks designed to improve visual
navigation performance. We propose three additional auxiliary tasks: predicting
the segmentation of the observation image and of the target image and
predicting the depth-map. These tasks enable the use of supervised learning to
pre-train a large part of the network and to reduce the number of training
steps substantially. The training performance has been further improved by
increasing the environment complexity gradually over time. An efficient neural
network structure is proposed, which is capable of learning for multiple
targets in multiple environments. Our method navigates in continuous state
spaces and on the AI2-THOR environment simulator outperforms state-of-the-art
goal-oriented visual navigation methods from the literature.

Robotics;Artificial Intelligence;Machine Learning;Machine Learning