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Some Considerations on Learning to Explore via Meta-Reinforcement Learning

2020-02-13 | |  81 |   50 |   0

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-图片.png . 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-图片.png deliver better performance than baseline algorithms on both tasks.

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