Meta-Learning surrogate models for sequential decision making
Abstract
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the decision making process. Crucially, this probabilistic model is chosen to be a Meta-Learning system that allows learning from a distribution of related problems, allowing data efficient adaptation to a target task. As a suitable instantiation of this framework, we explore the use of Neural processes due to statistical and computational desiderata. We apply our framework to a broad range of problem domains, such as control problems, recommender systems and adversarial attacks on RL agents, demonstrating an efficient and general black-box learning approach.
Cite
@article{arxiv.1903.11907,
title = {Meta-Learning surrogate models for sequential decision making},
author = {Alexandre Galashov and Jonathan Schwarz and Hyunjik Kim and Marta Garnelo and David Saxton and Pushmeet Kohli and S. M. Ali Eslami and Yee Whye Teh},
journal= {arXiv preprint arXiv:1903.11907},
year = {2019}
}