Toward Human-guided Safe Reinforcement Learning in the Real World

该项目旨在研究人类引导的安全强化学习（RL）。安全强化学习是一个重要的主题，它可以通过解决安全约束来实现强化学习系统的实际应用。现有的安全RL假定在数学或逻辑形式中存在指定的安全约束。本项目拟研究从人类直接提供的信息或通过语言模型间接提供的信息中学习安全目标，并在人类引导下进行持续修正以提高安全性。建立的理论和开发的算法将推动人工智能的前沿技术，并有助于安全强化学习的广泛实际应用，如机器人和自动驾驶，带来巨大的社会效益和经济效益。

This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits.