  The possibilities of robot control have multiplied across various domains
through the application of deep reinforcement learning. To overcome safety and
sampling efficiency issues, deep reinforcement learning models can be trained
in a simulation environment, allowing for faster iteration cycles. This can be
enhanced further by parallelizing the training process using GPUs. NVIDIA's
open-source robot learning framework Orbit leverages this potential by wrapping
tensor-based reinforcement learning libraries for high parallelism and building
upon Isaac Sim for its simulations. We contribute a detailed description of the
implementation of a benchmark reinforcement learning task, namely box pushing,
using Orbit. Additionally, we benchmark the performance of our implementation
in comparison to a CPU-based implementation and report the performance metrics.
Finally, we tune the hyper parameters of our implementation and show that we
can generate significantly more samples in the same amount of time by using
Orbit.
