mlsanityData to 7 Oct 2026

Meta Reinforcement Learning with Latent Variable Gaussian Processes

arXiv 1803.07551 ↗First posted 20 Mar 2018stat.ML cs.LGcited by 44

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Abstract (arXiv)

Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a model-based reinforcement learning setting and show that our meta-learning model effectively generalizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2018.

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Citing papers per year: 2019: 2; 2020: 1; 2021: 18; 2022: 6; 2023: 6; 2024: 3; 2025: 6; 2026: 2.

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  1. 2021Rapid Exploration for Open-World Navigation with Latent Goal Modelscited by 118
  2. 2020Offline Meta-Reinforcement Learning with Advantage Weightingcited by 92
  3. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 55
  4. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variablescited by 50
  5. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  6. 2021Context-Aware Safe Reinforcement Learning for Non-Stationary Environmentscited by 33
  7. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  8. 2019Watch, Try, Learn: Meta-Learning from Demonstrations and Rewardcited by 26
  9. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  10. 2021Model-based Meta Reinforcement Learning using Graph Structured Surrogate Modelscited by 16
  11. 2021CoMPS: Continual Meta Policy Searchcited by 15
  12. 2022A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learningcited by 15
32 more
  1. 2025Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planningcited by 12
  2. 2021PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulatorscited by 10
  3. 2022Continual Vision-based Reinforcement Learning with Group Symmetriescited by 10
  4. 2021Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learningcited by 8
  5. 2022Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarioscited by 8
  6. 2023Episodic Multi-Task Learning with Heterogeneous Neural Processescited by 8
  7. 2021MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerancecited by 7
  8. 2021Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Modelscited by 6
  9. 2023Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learningcited by 6
  10. 2025Linear Mixture Distributionally Robust Markov Decision Processescited by 5
  11. 2021Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processescited by 4
  12. 2021Bayesian Meta-Learning Through Variational Gaussian Processescited by 3
  13. 2022A model-based approach to meta-Reinforcement Learning: Transformers and tree searchcited by 3
  14. 2024Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfercited by 3
  15. 2025PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policiescited by 3
  16. 2021MetaKernel: Learning Variational Random Features with Limited Labelscited by 2
  17. 2023Learning How to Infer Partial MDPs for In-Context Adaptation and Explorationcited by 2
  18. 2024Systems with Switching Causal Relations: A Meta-Causal Perspectivecited by 2
  19. 2025Improving Controller Generalization with Dimensionless Markov Decision Processescited by 2
  20. 2021Adaptive Adversarial Training for Meta Reinforcement Learningcited by 1
  21. 2021Learning Robust Controllers Via Probabilistic Model-Based Policy Searchcited by 1
  22. 2023On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQRcited by 1
  23. 2024Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspectivecited by 1
  24. 2021Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environmentscited by 0
  25. 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Controlcited by 0
  26. 2021Learning to Transfer: A Foliated Theorycited by 0
  27. 2022Online No-regret Model-Based Meta RL for Personalized Navigationcited by 0
  28. 2023Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptationcited by 0
  29. 2025Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimizationcited by 0
  30. 2025Improving Forecasts of Suicide Attempts for Patients with Little Datacited by 0
  31. 2026MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shiftscited by 0
  32. 2026Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigationcited by 0