  In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain
Randomization to solve a navigation task in a natural environment relying
solely on a 2D laser scanner. We train a model-based RL agent in simulation to
follow lake and river shores and apply it on a real Unmanned Surface Vehicle in
a zero-shot setup. We demonstrate that even though the agent has not been
trained in the real world, it can fulfill its task successfully and adapt to
changes in the robot's environment and dynamics. Finally, we show that the RL
agent is more robust, faster, and more accurate than a state-aware
Model-Predictive-Controller.
