  Reinforcement learning (RL) is a promising field to enhance robotic autonomy
and decision making capabilities for space robotics, something which is
challenging with traditional techniques due to stochasticity and uncertainty
within the environment. RL can be used to enable lunar cave exploration with
infrequent human feedback, faster and safer lunar surface locomotion or the
coordination and collaboration of multi-robot systems. However, there are many
hurdles making research challenging for space robotic applications using RL and
machine learning, particularly due to insufficient resources for traditional
robotics simulators like CoppeliaSim. Our solution to this is an open source
modular platform called Reinforcement Learning for Simulation based Training of
Robots, or RL STaR, that helps to simplify and accelerate the application of RL
to the space robotics research field. This paper introduces the RL STaR
platform, and how researchers can use it through a demonstration.
