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

By Changkun Ye, Huimin Ma, Xiaoqin Zhang, Kai Zhang, Shaodi You. 2017

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Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “In order to enable DRL to escape local optima, speed up the training process and avoid danger conditions or accidents, Survival-Oriented Reinforcement Learning (SORL) model is proposed in [147], where survival is favored over maximizing total reward through”
Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019, background): “…speed up the training process and avoid danger conditions or accidents, Survival-Oriented Reinforcement Learning (SORL) model is proposed in (Ye et al., 2017), where survival is favored over maximizing total reward through modeling the autonomous driving problem as a constrained MDP and…”