Survival-Oriented Reinforcement Learning Model: An Effcient and Robust Deep Reinforcement Learning Algorithm for Autonomous Driving Problem

Changkun Ye, Huimin Ma, Xiaoqin Zhang, Kai Zhang, Shaodi You

Abstract: Using Deep Reinforcement Learning (DRL) algorithm to deal with autonomous driving tasks usually have unsatisfied performance due to lack of robustness and means to escape local optimum. In this article, we designs a Survival-Oriented Reinforcement Learning (SORL) model that tackle these problems by setting survival rather than maximize total reward as first priority. In SORL model, we model autonomous driving task as Constrained Markov Decision Process (CMDP) and introduce Negative-Avoidance Function to learn from previous failure. The SORL model greatly speed up the training process and improve the robustness of normal Deep Reinforcement Learning algorithm.