However, many of the (potential) weights are never accessed during the RLCI iterations, and may never need to be stored explicitly. To this end, sparse storage techniques for the learned weights may be utilized. For future work, it may be that moving beyond the linear parameterization of the state-action value function and utilizing deep neural networks can provide a more compact representation of the state and action space.