kaal:claim:4855607-013

Explainable reinforcement learning research has not yet produced usable explanations: the field relies on toy examples, omits user testing, produces explanations that are themselves complex, uses basic visualizations, and rarely open sources its code.

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Current research in explainable RL, which aims to make RL models more transparent and interpretable, also has limitations. These include the use of "toy examples", lack of user testing, complexity of explanations, basic visualizations, and lack of open-sourced code.
From

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Reinforcement Learning (RL), p. 26
https://ssrn.com/abstract=4855607 · source PDF

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Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

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failuresupport: evidencedfailure: Explainable RL immaturityfamily: ai-oversight-and-alignment-gapai-and-agents

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