Knowledge Reutilization in Meta-Reinforcement Learning

Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control. This coupling can obscure non-parametric task semantics, reduce sample efficiency, and limit cross-agent reuse. We propose a meta-knowledge reutilization framework that learns task-level knowledge on a dynamics-simplified agent and transfers it to heterogeneous agents. The framework uses a Bayesian non

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ethics.ai (16 June 2026), “Knowledge Reutilization in Meta-Reinforcement Learning,” evidence record 873, https://ethics.ai/record/873 (originally published by arXiv).

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