Papers › Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
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.
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