arxiv: 1803.01118 · v2 · pith:QW6OBNOLnew · submitted 2018-03-03 · 💻 cs.AI

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 This is my paper

classification 💻 cs.AI

keywords learninge-mamlexplorationmetareinforcementtextalgorithmsbetter

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We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$ deliver better performance on tasks where exploration is important.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
cs.LG 2025-10 unverdicted novelty 5.0

The work establishes OOD generalization bounds for meta-supervised learning and meta-RL that exploit MDP structure, then analyzes a gradient-based meta-RL algorithm.