In reinforcement learning, an agent is trained to take a series of actions in order to maximize a reward. Herein we follow much of the notation and framework laid out for the k-sparse eigenproblem in Ref 43 and apply it to the sCI case. The reinforcement learning process proceeds in a series of episodes, where the agent explores the environment and obtains rewards or penalties for its actions. In order to map the sCI procedure onto a reinforcement learning framework, it is necessary to define the state, the environment, actions, and rewards.