  Obstacle avoidance is a fundamental and challenging problem for autonomous
navigation of mobile robots. In this paper, we consider the problem of obstacle
avoidance in simple 3D environments where the robot has to solely rely on a
single monocular camera. In particular, we are interested in solving this
problem without relying on localization, mapping, or planning techniques. Most
of the existing work consider obstacle avoidance as two separate problems,
namely obstacle detection, and control. Inspired by the recent advantages of
deep reinforcement learning in Atari games and understanding highly complex
situations in Go, we tackle the obstacle avoidance problem as a data-driven
end-to-end deep learning approach. Our approach takes raw images as input and
generates control commands as output. We show that discrete action spaces are
outperforming continuous control commands in terms of expected average reward
in maze-like environments. Furthermore, we show how to accelerate the learning
and increase the robustness of the policy by incorporating predicted depth maps
by a generative adversarial network.
