Reinforcement Learning (RL) algorithms show amazing performance in recent years, but placing RL in real-world applications such as self-driven vehicles may suffer safety problems. A self-driven vehicle moving to a target position following a learned policy may suffer a vehicle with unpredictable aggressive behaviors or even being pursued by a vehicle following a Nash strategy. To address the safety issue of the self-driven vehicle in this scenario, this paper conducts a preliminary study based on a system of robots. A safe RL framework with safety guarantees is developed for a robot being pursued but with objectives covering more than capture-avoidance. Simulations and experiments are conducted based on the system of robots to evaluate the effectiveness of the developed safe RL framework.
@article{arxiv.2207.00842,
title = {Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance},
author = {Huanhui Cao and Zhiyuan Cai and Hairuo Wei and Wenjie Lu and Lin Zhang and Hao Xiong},
journal= {arXiv preprint arXiv:2207.00842},
year = {2022}
}