Some considerations on learning to explore via meta-reinforcement learning

OpenAI Blog 03/03/18, 08:00 AM Papers

Summary

OpenAI researchers introduce E-MAML and E-RL², two meta-reinforcement learning algorithms designed to improve exploration in tasks where discovering optimal policies requires significant exploration. The work demonstrates these algorithms' effectiveness on novel environments including Krazy World and maze tasks.

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# Some considerations on learning to explore via meta-reinforcement learning Source: [https://openai.com/index/some-considerations-on-learning-to-explore-via-meta-reinforcement-learning/](https://openai.com/index/some-considerations-on-learning-to-explore-via-meta-reinforcement-learning/) OpenAI## Abstract We consider the problem of exploration in meta reinforcement learning\. Two new meta reinforcement learning algorithms are suggested: E\-MAML and E\-RL²\. Results are presented on a novel environment we call "Krazy World" and a set of maze environments\. We show E\-MAML and E\-RL² deliver better performance on tasks where exploration is important\. - [Learning Paradigms](https://openai.com/research/index/?tags=learning-paradigms) ## Authors Bradly Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

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