# Reinforcement learning

`kaal:entity:reinforcement-learning`

**Status.** derived

This node is assembled mechanically from the 2 claims that carry the concept tag `reinforcement-learning`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

2 claims across 1 works, 2024 to 2024.

**2024**

- [4855607-013](https://wulfkaal.github.io/claims/4855607-013) [failure/evidenced] *(failure mode)* -- 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.
  > 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.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4855607-014](https://wulfkaal.github.io/claims/4855607-014) [failure/evidenced] *(failure mode)* -- Deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks.
  > Deep RL methods often demand large amounts of training data, suggesting that the algorithms may differ fundamentally from those underlying human learning. Moreover, learning without supervision is particularly hard when the reward is sparse, which is likely to happen for sequence generation tasks.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

## Verify

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    curl -s https://wulfkaal.github.io/entities/reinforcement-learning.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.