Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

12 papers of 8,653Sort Recent · Most cited
  1. 2026
    Adaptive Multi-Horizon Reinforcement LearningManoosh Samiei, Doina Precup, Paul MassetarXiv
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  2. 2026
    To Retain or to Adapt? Generalizing Continual LearningGiulia Lanzillotta, Mandana Samiei, Doina Precup … Claire VernadearXiv
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  3. 2026PDF ↗
  4. 2024
    Parseval Regularization for Continual Reinforcement LearningWesley Chung, Lynn Cherif, David Meger, Doina PrecupNeurIPS
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  5. 2024
    Learning Successor Features the Simple WayRaymond Chua, Arna Ghosh, Christos Kaplanis … Doina PrecupNeurIPS
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  6. 2023PDF ↗
  7. 2023
    A Definition of Continual Reinforcement LearningDavid Abel, André Barreto, Benjamin Van Roy … Satinder SinghNeurIPS
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  8. 2020
    Towards Continual Reinforcement Learning: A Review and PerspectivesKhimya Khetarpal, Matthew Riemer, Irina Rish, Doina PrecupJournal of Artificial Intelligence Research · Google DeepMind (United Kingdom) · McGill University · +2
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  9. 2022
    Continual Reinforcement Learning with Multi-Timescale Successor FeaturesRaymond Chua, Blake Richards, Doina Precup, Christos KaplanisConference on Cognitive Computational Neuroscience · McGill University · Google DeepMind (United Kingdom)
  10. 2020
    Keynote Lecture - Building Knowledge For AI AgentsWith Reinforcement LearningDoina PrecupIEEE 16th International Conference on Intelligent Compute… · Google DeepMind (United Kingdom) · McGill University
  11. 2019
    Building Knowledge for AI Agents with Reinforcement LearningDoina PrecupAdaptive Agents and Multi-Agents Systems · McGill University
  12. 2018
    The Barbados 2018 List of Open Issues in Continual LearningTom Schaul, Hado van Hasselt, Joseph Modayil … Doina PrecuparXiv
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.