A multi-agent deep reinforcement learning framework for automated driving on highways

We apply deep reinforcement learning to automated driving on highways. We propose a novel, simple framework with improved performance with respect to the state of the art. When implementing our algorithm on multilane highway scenarios, after the training phase, we observe via numerical simulations that the vehicles are able to avoid collisions and to reach their respective destination lanes with very high probability.