We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World'  and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where exploration is important.
Modifications to MAML and RL2 that should allow for better exploration.  The paper proposes a trick of extending objective functions to drive exploration in meta-RL on top of two recent meta-RL algorithms