  Deep Reinforcement Learning has been successfully applied in various computer
games [8]. However, it is still rarely used in real-world applications,
especially for the navigation and continuous control of real mobile robots
[13]. Previous approaches lack safety and robustness and/or need a structured
environment. In this paper we present our proof of concept for autonomous
self-learning robot navigation in an unknown environment for a real robot
without a map or planner. The input for the robot is only the fused data from a
2D laser scanner and a RGB-D camera as well as the orientation to the goal. The
map of the environment is unknown. The output actions of an Asynchronous
Advantage Actor-Critic network (GA3C) are the linear and angular velocities for
the robot. The navigator/controller network is pretrained in a high-speed,
parallel, and self-implemented simulation environment to speed up the learning
process and then deployed to the real robot. To avoid overfitting, we train
relatively small networks, and we add random Gaussian noise to the input laser
data. The sensor data fusion with the RGB-D camera allows the robot to navigate
in real environments with real 3D obstacle avoidance and without the need to
fit the environment to the sensory capabilities of the robot. To further
increase the robustness, we train on environments of varying difficulties and
run 32 training instances simultaneously. Video: supplementary File / YouTube,
Code: GitHub
