The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.