  We provide a simple hardware wrapper around the Quanser's
hardware-in-the-loop software development kit (HIL SDK) to allow for easy
development of new Quanser hardware. To connect to the hardware we use a module
written in Cython. The internal QuanserWrapper class handles most of the
difficult aspects of interacting with hardware, including the timing (using a
hardware timer), and ensuring the data sent to hardware is safe and correct,
where safety corresponds to safe operating voltage and current for the
specified hardware. Much of the recent success of Reinforcement learning (RL)
has been made possible with training and testing tools like OpenAI Gym and
Deepmind Control Suite. Unfortunately, tools for quickly testing and
transferring high-frequency RL algorithms from simulation to real hardware
environment remain mostly absent. We present Blue River Controls, a tool that
allows to train and test reinforcement learning algorithms on real-world
hardware. It features a simple interface based on OpenAI Gym, that works
directly on both simulation and hardware. We use Quanser's Qube Servo2-USB
platform, an underactuated rotary pendulum as an initial testing device. We
also provide tools to simplify training RL algorithms on other hardware.
Several baselines, from both classical controllers and pretrained RL agents are
included to compare performance across tasks. Blue River Controls is available
at this https URL: https://github.com/BlueRiverTech/quanser-openai-driver
