  Probabilistic Boolean Networks (PBNs) were introduced as a computational
model for the study of complex dynamical systems, such as Gene Regulatory
Networks (GRNs). Controllability in this context is the process of making
strategic interventions to the state of a network in order to drive it towards
some other state that exhibits favourable biological properties. In this paper
we study the ability of a Double Deep Q-Network with Prioritized Experience
Replay in learning control strategies within a finite number of time steps that
drive a PBN towards a target state, typically an attractor. The control method
is model-free and does not require knowledge of the network's underlying
dynamics, making it suitable for applications where inference of such dynamics
is intractable. We present extensive experiment results on two synthetic PBNs
and the PBN model constructed directly from gene-expression data of a study on
metastatic-melanoma.
