  We explore the potential to control terrain vehicles using deep reinforcement
in scenarios where human operators and traditional control methods are
inadequate. This letter presents a controller that perceives, plans, and
successfully controls a 16-tonne forestry vehicle with two frame articulation
joints, six wheels, and their actively articulated suspensions to traverse
rough terrain. The carefully shaped reward signal promotes safe, environmental,
and efficient driving, which leads to the emergence of unprecedented driving
skills. We test learned skills in a virtual environment, including terrains
reconstructed from high-density laser scans of forest sites. The controller
displays the ability to handle obstructing obstacles, slopes up to 27$^\circ$,
and a variety of natural terrains, all with limited wheel slip, smooth, and
upright traversal with intelligent use of the active suspensions. The results
confirm that deep reinforcement learning has the potential to enhance control
of vehicles with complex dynamics and high-dimensional observation data
compared to human operators or traditional control methods, especially in rough
terrain.
