Here we propose an alternative approach to the selected configuration interaction problem based on reinforcement learning, where the sCI problem is mapped to a sequential decisionmaking process that implicitly learns the optimal actions to take to return an accurate, compressed approximation to the FCI wave function. The optimal ranking of configurations can be learned on-the-fly due to observing the impact of including or removing (groups of) determinants on the ground state energy. After detailing the mathematical aspects of the reinforcement learning configuration interaction (RLCI) method, we explore the performance of RLCI methods against several prototypical cases, as well as some larger, strongly correlated hydrogen ring systems.