  We consider the problem of exploration in meta reinforcement learning. Two
new meta reinforcement learning algorithms are suggested: E-MAML and
E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy
World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$
deliver better performance on tasks where exploration is important.
