Toward Human-guided Safe Reinforcement Learning in the Real World

在现实世界中实现人类引导的安全强化学习

• 批准号: DP240102349
• 负责人: Prof Ling Chen
• 金额: $ 38.33万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240102349
• 关键词: Toward; Human; guided; Safe; Reinforcement

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.

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课题，它可以通过解决安全约束来实现强化学习系统的真实的应用。现有的安全RL假设数学或逻辑形式的指定安全约束的可用性。本项目旨在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，以及人为指导的持续纠正以改善安全。建立的理论和开发的算法将推进人工智能领域的前沿技术，并有助于安全RL的广泛真实的应用，如机器人和自动驾驶，带来巨大的社会和经济效益。