  Meta Reinforcement Learning (MRL) enables an agent to learn from a limited
number of past trajectories and extrapolate to a new task. In this paper, we
attempt to improve the robustness of MRL. We build upon model-agnostic
meta-learning (MAML) and propose a novel method to generate adversarial samples
for MRL by using Generative Adversarial Network (GAN). That allows us to
enhance the robustness of MRL to adversal attacks by leveraging these attacks
during meta training process.
