  Robust reinforcement learning is the problem of learning control policies
that provide optimal worst-case performance against a span of adversarial
environments. It is a crucial ingredient for deploying algorithms in real-world
scenarios with prevalent environmental uncertainties and has been a
long-standing object of attention in the community, without a standardized set
of benchmarks. This contribution endeavors to fill this gap. We introduce the
Robust Reinforcement Learning Suite (RRLS), a benchmark suite based on Mujoco
environments. RRLS provides six continuous control tasks with two types of
uncertainty sets for training and evaluation. Our benchmark aims to standardize
robust reinforcement learning tasks, facilitating reproducible and comparable
experiments, in particular those from recent state-of-the-art contributions,
for which we demonstrate the use of RRLS. It is also designed to be easily
expandable to new environments. The source code is available at
\href{https://github.com/SuReLI/RRLS}{https://github.com/SuReLI/RRLS}.
