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- W2891227010 abstract "We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-$text{RL}^2$. Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning. Further results are presented on a set of maze environments. We show E-MAML and E-$text{RL}^2$ deliver better performance than baseline algorithms on both tasks." @default.
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- W2891227010 date "2018-01-01" @default.
- W2891227010 modified "2023-09-24" @default.
- W2891227010 title "The Importance of Sampling inMeta-Reinforcement Learning" @default.
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