# Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles

## TL;DR

This paper introduces a reinforcement learning-based driving policy for autonomous vehicles on freeways, capable of operating in mixed traffic environments without relying on detailed environmental models.

## Contribution

It presents one of the first reinforcement learning policies for mixed autonomous and manual traffic, avoiding assumptions about environment dynamics.

## Key findings

- RL policy performs comparably to optimal control methods.
- The policy is effective in realistic traffic scenarios.
- Initial results show autonomous vehicle behavior impacts traffic flow.

## Abstract

In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow are presented.