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.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
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abstract
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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cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
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.