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

Some Considerations on Learning to Explore via Meta-Reinforcement Learning