  Nature provides a way to understand physics with reinforcement learning since
nature favors the economical way for an object to propagate. In the case of
classical mechanics, nature favors the object to move along the path according
to the integral of the Lagrangian, called the action $\mathcal{S}$. We consider
setting the reward/penalty as a function of $\mathcal{S}$, so the agent could
learn the physical trajectory of particles in various kinds of environments
with reinforcement learning. In this work, we verified the idea by using a
Q-Learning based algorithm on learning how light propagates in materials with
different refraction indices, and show that the agent could recover the
minimal-time path equivalent to the solution obtained by Snell's law or
Fermat's Principle. We also discuss the similarity of our reinforcement
learning approach to the path integral formalism.
