论文标题
为了追求机器人的安全加强学习，但目标涵盖了避免捕获
Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance
论文作者
论文摘要
近年来，增强学习（RL）算法表现出惊人的性能，但是将RL放置在实际应用中，例如自动驱动车辆可能会遇到安全问题。在学习政策之后，自动驱动的车辆移至目标位置可能会遭受具有不可预测的侵略性行为的车辆，甚至遵循纳什策略，甚至被车辆追赶。为了在这种情况下解决自动驱动车辆的安全问题，本文根据机器人系统进行了初步研究。为了追求一个机器人，开发了具有安全保证的安全RL框架，但目标涵盖的目标不仅仅是避免捕获。模拟和实验是根据机器人系统进行的，以评估开发的安全RL框架的有效性。
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.
