# Autonomous Driving with Reinforcement Learning and Rule-based Policies

Amarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Riccardo Giol, Marcello Restelli, Danilo Romano, and Andrea Alessandretti

Workshop on AI for Autonomous Driving (AIAD) @ICML 2020, 2020.

Abstract
The design of high-level decision-making systems is a topical problem in the field of autonomous driving. In this paper, we combine rule-based strategies and reinforcement learning (RL) with the goal of achieving transparency and robustness of the learned controllers while maintaining the generality of a policy learned by environment interaction. We combine the best properties of these two worlds by designing parametric rule-based controllers, in which interpretable rules can be provided by domain experts and their parameters are learned via RL. After illustrating how to apply parameter-based RL methods (PGPE), we present numerical simulations in the highway and in two urban scenarios: intersection and roundabout.

 @article{likmeta2020autonomous,
}