# Meta Reinforcement Learning with Latent Variable Gaussian Processes

arXiv: https://arxiv.org/abs/1803.07551 · HTML: https://mlsanity.com/paper/1803.07551/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 30 of 44)

- [Rapid Exploration for Open-World Navigation with Latent Goal Models](https://mlsanity.com/paper/2104.05859/) (2021)
- [Offline Meta-Reinforcement Learning with Advantage Weighting](https://mlsanity.com/paper/2008.06043/) (2020)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [Meta-Inverse Reinforcement Learning with Probabilistic Context Variables](https://mlsanity.com/paper/1909.09314/) (2019)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Context-Aware Safe Reinforcement Learning for Non-Stationary Environments](https://mlsanity.com/paper/2101.00531/) (2021)
- [Watch, Try, Learn: Meta-Learning from Demonstrations and Reward](https://mlsanity.com/paper/1906.03352/) (2019)
- [Conditioning Sparse Variational Gaussian Processes for Online Decision-making](https://mlsanity.com/paper/2110.15172/) (2021)
- [Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models](https://mlsanity.com/paper/2102.08291/) (2021)
- [CoMPS: Continual Meta Policy Search](https://mlsanity.com/paper/2112.04467/) (2021)
- [A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2206.04551/) (2022)
- [Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning](https://mlsanity.com/paper/2502.19009/) (2025)
- [PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators](https://mlsanity.com/paper/2102.06961/) (2021)
- [Continual Vision-based Reinforcement Learning with Group Symmetries](https://mlsanity.com/paper/2210.12301/) (2022)
- [Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning](https://mlsanity.com/paper/2103.06386/) (2021)
- [MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance](https://mlsanity.com/paper/2112.03575/) (2021)
- [Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios](https://mlsanity.com/paper/2206.14697/) (2022)
- [Episodic Multi-Task Learning with Heterogeneous Neural Processes](https://mlsanity.com/paper/2310.18713/) (2023)
- [Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models](https://mlsanity.com/paper/2102.11394/) (2021)
- [Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning](https://mlsanity.com/paper/2311.07558/) (2023)
- [Linear Mixture Distributionally Robust Markov Decision Processes](https://mlsanity.com/paper/2505.18044/) (2025)
- [Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes](https://mlsanity.com/paper/2112.00882/) (2021)
- [Bayesian Meta-Learning Through Variational Gaussian Processes](https://mlsanity.com/paper/2110.11044/) (2021)
- [A model-based approach to meta-Reinforcement Learning: Transformers and tree search](https://mlsanity.com/paper/2208.11535/) (2022)
- [Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer](https://mlsanity.com/paper/2405.03534/) (2024)
- [PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies](https://mlsanity.com/paper/2502.11377/) (2025)
- [MetaKernel: Learning Variational Random Features with Limited Labels](https://mlsanity.com/paper/2105.03781/) (2021)
- [Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration](https://mlsanity.com/paper/2302.04250/) (2023)
- [Systems with Switching Causal Relations: A Meta-Causal Perspective](https://mlsanity.com/paper/2410.13054/) (2024)

## 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.