  Designing RNA molecules has garnered recent interest in medicine, synthetic
biology, biotechnology and bioinformatics since many functional RNA molecules
were shown to be involved in regulatory processes for transcription,
epigenetics and translation. Since an RNA's function depends on its structural
properties, the RNA Design problem is to find an RNA sequence which satisfies
given structural constraints. Here, we propose a new algorithm for the RNA
Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train
a policy network to sequentially design an entire RNA sequence given a
specified target structure. By meta-learning across 65000 different RNA Design
tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA
Design policy that can be applied out of the box to solve novel RNA Design
tasks. Methodologically, for what we believe to be the first time, we jointly
optimize over a rich space of architectures for the policy network, the
hyperparameters of the training procedure and the formulation of the decision
process. Comprehensive empirical results on two widely-used RNA Design
benchmarks, as well as a third one that we introduce, show that our approach
achieves new state-of-the-art performance on the former while also being orders
of magnitudes faster in reaching the previous state-of-the-art performance. In
an ablation study, we analyze the importance of our method's different
components.
