Herein we will utilize a Q-learning approach, wherein the goal is to learn the optimal state-action value function Q(s, a). Although several different RL approaches could be used, Q-learning is advantageous for its simplicity, robustness in planning, and ability to learn the optimal policy while following a different exploration policy. In Q-learning, the learned function Q(s, a) returns the value of taking a particular action a out of a state s. For a given set s of determinants that make up the current sCI space, possible actions a are removing the p-th determinant from the current set and replacing it with a SD q that is outside the current sCI space, i.e., a = (p, q), for p ∈ s and q / ∈ s