Employing Batch Reinforcement Learning to Control Gene Regulation Without Explicitly Constructing Gene Regulatory Networks
The goal of controlling a gene regulatory network (GRN) is to generate an intervention strategy, i.e., a control policy, such that by applying the policy the system will avoid undesirable states. In this work, we propose a method to control GRNs by using Batch Mode Reinforcement Learning (Batch RL). Our idea is based on the fact that time series gene expression data can actually be interpreted as a sequence of experience tuples collected from the environment. Existing studies on this control task try to infer a model using gene expression data and then calculate a control policy over the constructed model. However, we propose a method that can directly use the available gene expression data to obtain an approximated control policy for gene regulation that avoids the time consuming model building phase. Results show that we can obtain policies for gene regulation systems of several thousands of genes just in several seconds while existing solutions get stuck for even tens of genes. Interestingly, the reported results also show that our method produces policies that are almost as good as the ones generated by existing model dependent methods.