The Importance of Sampling inMeta-Reinforcement Learning

NeurIPS 2018 Bradly StadieGe YangRein HouthooftPeter ChenYan DuanYuhuai WuPieter AbbeelIlya Sutskever

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-$\text{RL}^2$... (read more)

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