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arxiv_llm_reliability · Nov 11, 2026 · paper

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ReShap: Interpretable Reinforcement Learning for High-Dimensional Multi-Action Settings

arxiv.orgNov 11, 2026
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In brief

Deep Reinforcement Learning (RL) plays a crucial role in tackling complex tasks, yet its reliance on deep neural networks makes the learned policies opaque. To enhance the trustworthiness of RL policies, existing work (e.g., the SILVER framework [Li et al., 2025]) has focused on

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ReShap: Interpretable Reinforcement Learning for High-Dimensional Multi-Action Settings

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