  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.
