This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation
of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection
and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator
Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural
Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the
3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability
scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated
experiments and favorably compared with a previous reactive navigation approach on the same UGV.
