# kaal:claim:4855607-013

**Claim.** 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.

**Type.** failure  **Support.** evidenced

**Holds when.**

- current explainable RL research as surveyed in the text

**Source quote.**

> 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), page 26

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

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Explainable RL immaturity  (family: ai-oversight-and-alignment-gap)

**Topics.** ai-and-agents

**Keywords.** reinforcement-learning, explainable-ai, toy-examples, user-testing, reproducibility

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.