  A reinforcement learning (RL) policy trained in a nominal environment could
fail in a new/perturbed environment due to the existence of dynamic variations.
Existing robust methods try to obtain a fixed policy for all envisioned dynamic
variation scenarios through robust or adversarial training. These methods could
lead to conservative performance due to emphasis on the worst case, and often
involve tedious modifications to the training environment. We propose an
approach to robustifying a pre-trained non-robust RL policy with
$\mathcal{L}_1$ adaptive control. Leveraging the capability of an
$\mathcal{L}_1$ control law in the fast estimation of and active compensation
for dynamic variations, our approach can significantly improve the robustness
of an RL policy trained in a standard (i.e., non-robust) way, either in a
simulator or in the real world. Numerical experiments are provided to validate
the efficacy of the proposed approach.
