Here we have explored the potential of using reinforcement learning techniques to solve the selected configuration interaction problem. In the prototypical cases explored, RLCI outperformed HCI in terms of generating more compact wave functions without neglecting chemical accuracy (< 1 kcal/mol from FCI). Although we do not claim that the current implementation is necessarily faster than existing sCI methods, we have provided support that approaches based on reinforcement learning may yield more optimally compact wave functions, at least at the determinantal level. In these dissociation curves presented here, HCI appears to require on the order of 2-3× the number of determinants for comparable accuracy to RLCI. As is seen in the strongly correlated case of the stretched H 8 chain, there may be an upper limit to how compressed a wave function can become. In these cases, other wave function ansätze may be required, or it may speak to the importance of orbital optimization in the strongly correlated cases. Both avenues are worth pursuing with a RL approach.