  A major challenge of reinforcement learning (RL) in real-world applications
is the variation between environments, tasks or clients. Meta-RL (MRL)
addresses this issue by learning a meta-policy that adapts to new tasks.
Standard MRL methods optimize the average return over tasks, but often suffer
from poor results in tasks of high risk or difficulty. This limits system
reliability since test tasks are not known in advance. In this work, we define
a robust MRL objective with a controlled robustness level. Optimization of
analogous robust objectives in RL is known to lead to both *biased gradients*
and *data inefficiency*. We prove that the gradient bias disappears in our
proposed MRL framework. The data inefficiency is addressed via the novel Robust
Meta RL algorithm (RoML). RoML is a meta-algorithm that generates a robust
version of any given MRL algorithm, by identifying and over-sampling harder
tasks throughout training. We demonstrate that RoML achieves robust returns on
multiple navigation and continuous control benchmarks.
