The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design. RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered. We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds. To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity. On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned.
Contribution
This paper apply a model-based RL algorithm, DyNA-PPO for designing biological sequences. By being model-based, this algorithm is sample efficiency compared to model-free RL algorithms. This advantage is attractive and important in the context of biological sequence design since the designed is constrained to be done in the large batch / low round settings. To further improves model efficiency, the authors reduce learning bias by quantifying the reliability and automatically selecting models of appropriate complexity via cross validation. To encourage diversity in the target distribution they also penalize the reward using a visitation-based strategy.
Clarity
Overall, the paper is well written, well motivated and well structured. The technical content is also very clear and good.
Novelty
The novelty in this work seems to be more on the applicative side (RL to optimizing DNA and protein sequences) than the method itself. I agree with the authors that most existing optimization methods are ill equipped for the large batch / low round settings and as sample efficiency becomes critically important as the number of round gets lower and their method is a good solution in such settings. 
The technical novelty seems incremental as cross-validating and using a set of models under particular performance constraints does not constitutes a novel contribution. Penalizing the reward if the same sequence is seen multiple times seems decent and works well compared to the entropy regularization but it is still questionable if it is the best solution for biological sequences. Showing results when the reward is penalized using the hamming loss or the biological similarity  with previous sequences could go a long way to convince of your choice.
Experiments:
The experiments are overall well presented and seems robust given the number of replicates that was made each time. 
Analyzing the model performances across different metrics: diversity, fraction of optimals, cumulative maximum helped to  understand the method and its advantages. 
However, I would like to see other RL algorithms that were shown in the appendix for all those comparisons.
Including MCMC methods in the experiments will also allow to see how RL methods compared to  the sota in bioinformatics.
For the Icing dataset, you mention that it is a contribution but do not provide enough details regarding it to allow further research with it.
In a real life biological setting, the data obtained at each batch will be more likely different both in term of sequences but also in term of labels. How all your method (and the others) perform with changing distribution of data from one batch to another. Ex (e.g. 0 < y batch 1 < 100, 100 <= y batch 2 < 500, etc) ?
You tried two values for R^2 in one experiment (one positive and one negative). What happens for any other positive values (e.g 0.1. 0.2, 0.3, etc) ?
Points of improvement
Given the applicative nature of the paper and the proposed method there are few small experiments that could have been done to strengthen the manuscript (see questions and comments above). 
Preliminary rating:
* weak accept *