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Reinforcement learning

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Learning to maximise a reward signal through trial and feedback 1. Its characteristic failure is reward hacking, so the reward is recorded as the system's objective and evals look for unintended strategies.

Developed in
ch. 11, By learning paradigm
Chapters
ch. 11, AI Defined
Source
1 numbered reference, listed below

Where it is used

The term is not used under this name in running prose; the sections listed under "Developed in" treat it.

  • Machine learning

    The branch of AI in which a system improves at a task by learning patterns from data rather than by following rules people wrote.

  • Reinforcement learning from human feedback (RLHF)

    A way to align a pre-trained model: supervised fine-tuning on human demonstrations, then reinforcement learning against a reward model trained on human…

  • Reward hacking

    A system finding an unintended way to maximise its reward or objective without doing what its designers meant.

  • Self-supervised learning

    Learning by predicting parts of the input itself, such as the next token, over large corpora; the AI Act's definition of a general-purpose model names…

  • Supervised learning

    Learning from labelled examples.

  • Unsupervised learning

    Learning structure (clusters, anomalies) from data without labels.

  • Evals

    Automated tests of a model's or agent's behaviour (capability, safety and adversarial), run as controls, not as one-off research.

Sources

  1. [1] ISO/IEC 22989:2022, Artificial intelligence concepts and terminology (referenced by identifier only; autonomy and heteronomy; clause 5.11 machine learning approaches: supervised, unsupervised, semi-supervised, reinforcement). ISO/IEC. 2022-07. https://www.iso.org/standard/74296.html (verified: secondary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Reinforcement learning. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/reinforcement-learning. CC BY 4.0

BibTeX

@misc{aige2026reinforcementlearning,
  author  = {Jorge García Aibar},
  title   = {{Reinforcement learning}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/reinforcement-learning}
}