  This work regards our preliminary investigation on the problem of path
planning for autonomous vehicles that move on a freeway. We approach this
problem by proposing a driving policy based on Reinforcement Learning. The
proposed policy makes minimal or no assumptions about the environment, since no
a priori knowledge about the system dynamics is required. We compare the
performance of the proposed policy against an optimal policy derived via
Dynamic Programming and against manual driving simulated by SUMO traffic
simulator.
