  We show that the equations of reinforcement learning and light transport
simulation are related integral equations. Based on this correspondence, a
scheme to learn importance while sampling path space is derived. The new
approach is demonstrated in a consistent light transport simulation algorithm
that uses reinforcement learning to progressively learn where light comes from.
As using this information for importance sampling includes information about
visibility, too, the number of light transport paths with zero contribution is
dramatically reduced, resulting in much less noisy images within a fixed time
budget.
