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.
Generalization error bounds using wasserstein distances
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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.