Meta Reinforcement Learning with Latent Variable Gaussian Processes

Steindor Saemundsson, Katja Hofmann, Marc Peter Deisenroth
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:641-651, 2018.

Abstract

Learning from small data sets is critical in many practical applications where data col- lection 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 general- izing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or re- lies in some other way on human expertise. In this paper, we frame meta learning as a hi- erarchical 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 gen- eralizes 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-saemundsson18a, title = {Meta Reinforcement Learning with Latent Variable {G}aussian Processes}, author = {Saemundsson, Steindor and Hofmann, Katja and Deisenroth, Marc Peter}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {641--651}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/saemundsson18a/saemundsson18a.pdf}, url = {https://proceedings.mlr.press/r16/saemundsson18a.html}, abstract = {Learning from small data sets is critical in many practical applications where data col- lection 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 general- izing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or re- lies in some other way on human expertise. In this paper, we frame meta learning as a hi- erarchical 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 gen- eralizes 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Meta Reinforcement Learning with Latent Variable Gaussian Processes %A Steindor Saemundsson %A Katja Hofmann %A Marc Peter Deisenroth %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-saemundsson18a %I PMLR %P 641--651 %U https://proceedings.mlr.press/r16/saemundsson18a.html %V R16 %X Learning from small data sets is critical in many practical applications where data col- lection 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 general- izing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or re- lies in some other way on human expertise. In this paper, we frame meta learning as a hi- erarchical 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 gen- eralizes 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. %Z Reissued by PMLR on 04 October 2026.
APA
Saemundsson, S., Hofmann, K. & Deisenroth, M.P.. (2018). Meta Reinforcement Learning with Latent Variable Gaussian Processes. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:641-651 Available from https://proceedings.mlr.press/r16/saemundsson18a.html. Reissued by PMLR on 04 October 2026.

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