  Reinforcement learning control of an underground loader is investigated in
simulated environment, using a multi-agent deep neural network approach. At the
start of each loading cycle, one agent selects the dig position from a depth
camera image of the pile of fragmented rock. A second agent is responsible for
continuous control of the vehicle, with the goal of filling the bucket at the
selected loading point, while avoiding collisions, getting stuck, or losing
ground traction. It relies on motion and force sensors, as well as on camera
and lidar. Using a soft actor-critic algorithm the agents learn policies for
efficient bucket filling over many subsequent loading cycles, with clear
ability to adapt to the changing environment. The best results, on average 75%
of the max capacity, are obtained when including a penalty for energy usage in
the reward.
