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.

该项目旨在调查人类引导的安全加强学习（RL）。安全RL是一个重要的主题，可以通过解决安全限制来实现RL系统的真实应用。现有的安全RL假设在数学或逻辑形式中可以使用指定的安全限制。该项目建议通过人类直接或间接通过语言模型提供的信息来研究学习安全目标，并通过人类引导的持续纠正以改进安全性。既定的理论和开发的算法将推进AI的前沿技术，并为安全RL的各种真实应用做出贡献，例如机器人技术和自动驾驶，从而带来了巨大的社会和经济利益。