Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization

• URL: http://arxiv.org/abs/2209.06259v1
• Date: Tue, 13 Sep 2022 18:37:27 GMT
• Title: Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
• Authors: Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon
• Abstract summary: We train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results.
• Score: 68.28697120944116
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.

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