We show that the equations for reinforcement learning and light transport simulation are related integral equations. After a brief introduction of reinforcement learning and light transport simulation we visualize the correspondence between the equations by pattern matching. Based on this correspondence, a scheme to learn importance during sampling path space is derived. The new approach is demonstrated in a consistent light transport simulation algorithm that uses reinforcement learning to progressively learn probability density functions for importance sampling. Furthermore we show that our method is easy to integrate into any existing path tracer and can greatly increase rendering efficiency.