  Despite the success achieved by the analysis of supervised learning
algorithms in the framework of statistical mechanics, reinforcement learning
has remained largely untouched. Here we move towards closing the gap by
analyzing the dynamics of the policy gradient algorithm. For a convex problem,
we show that it obeys a drift-diffusion motion with coeffcients tuned by
learning rate. Furthermore, we propose a mapping between a non-convex
reinforcement learning problem and a disordered system. This mapping enables us
to show how the learning rate acts as an effective temperature and thus is
capable of smoothing rough landscapes, corroborating what is displayed by the
drift-diffusive description and paving the way for physics-inspired algorithmic
optimization based on annealing procedures in disordered systems.
