  Success stories of applied machine learning can be traced back to the
datasets and environments that were put forward as challenges for the
community. The challenge that the community sets as a benchmark is usually the
challenge that the community eventually solves. The ultimate challenge of
reinforcement learning research is to train real agents to operate in the real
environment, but until now there has not been a common real-world RL benchmark.
In this work, we present a prototype real-world environment from OffWorld Gym
-- a collection of real-world environments for reinforcement learning in
robotics with free public remote access. Close integration into existing
ecosystem allows the community to start using OffWorld Gym without any prior
experience in robotics and takes away the burden of managing a physical
robotics system, abstracting it under a familiar API. We introduce a navigation
task, where a robot has to reach a visual beacon on an uneven terrain using
only the camera input and provide baseline results in both the real environment
and the simulated replica. To start training, visit https://gym.offworld.ai
