Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Abstract

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.