  We study the problem of experiment design to learn causal structures from
interventional data. We consider an active learning setting in which the
experimenter decides to intervene on one of the variables in the system in each
step and uses the results of the intervention to recover further causal
relationships among the variables. The goal is to fully identify the causal
structures with minimum number of interventions. We present the first deep
reinforcement learning based solution for the problem of experiment design. In
the proposed method, we embed input graphs to vectors using a graph neural
network and feed them to another neural network which outputs a variable for
performing intervention in each step. Both networks are trained jointly via a
Q-iteration algorithm. Experimental results show that the proposed method
achieves competitive performance in recovering causal structures with respect
to previous works, while significantly reducing execution time in dense graphs.
