d context into a predefined Building the Network of the Future set of policies/actions. At runtime, the deep learner can select the most likely policy or action. The learner can dynamically update its model based on positive and negative reinforcements. The back- propagation algorithm can be used to update the a posteriori probabilities to best reflect the correct action/policy. For example, if the learner predicted a no-action policy, and an action was necessary then the learner will negatively emphasize a "no action" and positively emphasize the correct action. The pitfall with deep learning is that one is making an instant decision in time and not optimiz- ing a sequence of decisions toward a common goal. This is fine when recognizing an object in an image, but present a serious issue when needing to recommend a set of actions on how best to migrate traffic from one network node to another, for example. There are two potential solutions. The first is to use dynamic programming or a form of Markov model, such as that adopted in speech recognition. The basic concept is a set of different decisions with associated probabilities (or costs) computed at each time instant. Dynamic programming is then applied to find the best path based on minimizing a cumulative objective function. The second approach is reinforcement learning, which is a form of ML [Mit97]. The learner receives reinforcement about its action but not being told the exact action. Reinforcement learning is typically formulated based on a Markov decision process. The objective is to maximize the credit assignment, or reward. A reinforcement learner can take an action based on its current state and reward input. The combination of deep learning and reinforcement learning to form a more holistic and powerful AI system has been explored most recently for gaming. The DeepMind's AlphaGo program beat world champion Lee Sedol in four out of five games of Go, a complex board game [Gog16F]. The system combined Monte Carlo tree search with deep neural networks th