Meta-Reinforcement Learning

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Overview

Meta-reinforcement learning studies how an agent can use experience across many related tasks to learn a new task with little additional interaction. Common approaches include gradient-based methods such as MAML, recurrent or context-conditioned policies that infer tasks from interaction histories, and Bayesian or world-model methods that represent task uncertainty and predict outcomes. Researchers also use hierarchical skills, population-based search, and language or reflection signals to support adaptation in settings from robot control to multi-agent games and language agents. A central challenge is making adaptation generalize to unfamiliar task distributions while remaining data-efficient and safe, especially when task context is ambiguous or learned models are unreliable.

Latest papers

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40 papers4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation

    Mar 5, 2026Rifny Rachman, Josh Tingey, Richard Allmendinger +3Supply Chain OptimizationMulti-Objective Reinforcement Learning

    The University of Manchester, United Kingdom · Peak AI, Ltd, United Kingdom

  2. MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation

    Mar 4, 2026Lu Yang, Zelai Xu, Minyang Xie +4RL for LLM AgentsMeta-Reinforcement Learning

    Tsinghua University

  3. Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning

    Mar 3, 2026Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1Representation LearningOffline RL

    Aalto University · Toyota Research Institute · Vrije Universiteit Amsterdam

  4. Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning

    Mar 1, 2026Ke Sun, Hongming Zhang, Jun Jin +4Reinforcement LearningContinual RL

    University of Alberta · National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences · Edmonton Research Center, Huawei Canada +2

  5. Hereditary Geometric Meta-RL: Nonlocal Generalization via Task Symmetries

    Feb 28, 2026Paul Nitschke, Shahriar TalebiSymmetry DiscoveryMeta-Reinforcement Learning

    P. Nitschke is with the Paulson School Of Engineering And Applied Sciences, Harvard University, Cambridge, US. · S. Talebi is with the UCLA Samueli School of Engineering and Applied Science, University of California, Los Angeles, US.

  6. Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent

    Feb 23, 2026Björn Hoppmann, Christoph ScholzMeta-LearningMeta-Reinforcement Learning

  7. Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

    Jan 26, 2026Shobhita Sundaram, John Quan, Ariel Kwiatkowski +3Self-Play RLRL for Language Model Reasoning

    1MIT · 2Meta FAIR · 3New York University

  8. Meta-RL with Bayesian Linear Task Models

    Dec 24, 2025Jingyang You, Hanna KurniawatiRepresentation LearningReinforcement Learning

    School of Computing Australian National University

  9. Dual-Evolving Reinforcement Learning for Automated Reward Design

    Dec 15, 2025Sitao Cheng, Tianle Li, Xuhan Huang +2Reward Design for RLReinforcement Learning

    University of Waterloo · The University of Hong Kong · The Chinese University of Hong Kong, Shenzhen +1

  10. Meta-reinforcement learning with minimum attention

    May 22, 2025Shashank Gupta, Pilhwa LeeMeta-Reinforcement LearningPolicy Learning

    Department of EECS University of Michigan Ann Arbor, MI 48109 · Department of Mathematics Morgan State University Baltimore, MD 21251