mlsanityData to 13 Oct 2026

A Tutorial on Meta-Reinforcement Learning

arXiv 2301.08028 ↗First posted 19 Jan 2023cs.LGcited by 104

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Cited by 38 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.

Key facts. Cited by 104 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Jan 2023.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 24; 2024: 32; 2025: 29; 2026: 19.

104 papers.

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  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,433
  2. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 135
  3. 2025Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuningcited by 122
  4. 2023Structured State Space Models for In-Context Reinforcement Learningcited by 109
  5. 2025Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thoughtcited by 109
  6. 2023Diffusion Models for Reinforcement Learning: A Surveycited by 85
  7. 2023Generalization to New Sequential Decision Making Tasks with In-Context Learningcited by 39
  8. 2025Meta-World+: An Improved, Standardized, RL Benchmarkcited by 38
  9. 2024Doubly Mild Generalization for Offline Reinforcement Learningcited by 21
  10. 2025Meta-RL Induces Exploration in Language Agentscited by 20
  11. 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policiescited by 19
  12. 2024Offline Reinforcement Learning with OOD State Correction and OOD Action Suppressioncited by 17
38 more
  1. 2025A Survey of Continual Reinforcement Learningcited by 17
  2. 2023Structure in Deep Reinforcement Learning: A Survey and Open Problemscited by 16
  3. 2023Discovering General Reinforcement Learning Algorithms with Adversarial Environment Designcited by 16
  4. 2024Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learningcited by 16
  5. 2026RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedbackcited by 16
  6. 2023Recurrent Hypernetworks are Surprisingly Strong in Meta-RLcited by 15
  7. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  8. 2024Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQRcited by 14
  9. 2024Contextual Bilevel Reinforcement Learning for Incentive Alignmentcited by 14
  10. 2024Can Learned Optimization Make Reinforcement Learning Less Difficult?cited by 14
  11. 2023Thinker: Learning to Plan and Actcited by 13
  12. 2023Asking Before Acting: Gather Information in Embodied Decision Making with Language Modelscited by 12
  13. 2025Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environmentscited by 11
  14. 2026Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuningcited by 11
  15. 2025Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Surveycited by 10
  16. 2023A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learningcited by 9
  17. 2023An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descentcited by 9
  18. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  19. 2025Yes, Q-learning Helps Offline In-Context RLcited by 9
  20. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcited by 8
  21. 2024Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Datacited by 8
  22. 2025Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamicscited by 8
  23. 2023Improving Intrinsic Exploration by Creating Stationary Objectivescited by 7
  24. 2024Hierarchical Transformers are Efficient Meta-Reinforcement Learnerscited by 7
  25. 2024ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learningcited by 7
  26. 2024TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characterscited by 6
  27. 2024Certifiably Robust Policies for Uncertain Parametric Environmentscited by 6
  28. 2026Generalization in Online Reinforcement Learning for Mobile Agentscited by 6
  29. 2024Deep Reinforcement Learning for Modelling Protein Complexescited by 5
  30. 2025STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planningcited by 5
  31. 2025MetaSym: A Symplectic Meta-learning Framework for Physical Intelligencecited by 5
  32. 2025Scalable In-Context Q-Learningcited by 5
  33. 2023First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offscited by 4
  34. 2024ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AIcited by 4
  35. 2025Learning Policy Committees for Effective Personalization in MDPs with Diverse Taskscited by 4
  36. 2025Reinforcement Learning for Individual Optimal Policy from Heterogeneous Datacited by 4
  37. 2023Bayesian Exploration Networkscited by 3
  38. 2024DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learningcited by 3

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