Data-efficient Safe Control with Recovery-to-Optimality Guarantees

数据高效的安全控制，并保证恢复最佳性

• 批准号: 2227311
• 负责人: Bahare Kiumarsi
• 金额: $ 40万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227311&HistoricalAwards=false
• 关键词: Data; efficient; Safe; Control; Recovery

As rapid developments on learning-enabled systems in recent years have been advancing autonomy capabilities of systems, their safety certification becomes exceedingly important. While the recent progress on safe reinforcement learning (RL) algorithms for autonomous control design has been promising, these algorithms are accountable only in stable environments and under the availability of comprehensive and high-quality data sets. However, many systems must operate in unpredictable environments under which dangerous divergence might arise between safety and performance. In these environments, adaptation of safety and performance specifications to the context is required. Besides, RL agent must perform learning under realistic data quantity and quality. Current RL practice assumes availability of rich and high-quality data with full observability of the entire system’s states. These assumptions can be violated in many practical systems. This award supports research to create low-complexity safe learning-enabled algorithms for partially observable systems that are equipped with highly-efficient conflict management mechanisms to deliver as much performance as possible safely. Advances will have broad implications in applications of autonomous systems, robots, manufacturing, smart grids, and more.This research project aims to develop low-complexity, safe learning-enabled algorithms for partially observable systems equipped with highly efficient conflict management mechanisms. The objectives of this project are two-fold: 1) Proposing direct data-driven learning approaches for backup safe control policies in partially observable nonlinear systems with uncertain dynamics. The utilization of concepts such as L-extra sample dynamics, probabilistic contractivity, and convex lifting will enable the learning of safe control policies for nonlinear systems with nonconvex safe sets using only measured noisy input-output data. 2) Introducing novel merging approaches to proactively manage conflicts by merging learned backup safe control policies with learning-enabled control policies. Instead of providing reactive quick fixes to conflicts as they arise, these approaches will enable proactive conflict management to avoid destructive future conflicts. Towards conflict management, the level sets of the RL agent will be adapted to the situation to make the agent align with the safety constraint. That is, safety-shaped value functions will be learned to effectively resolve conflicts by considering safety and optimality concerns across the relevant domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，学习型系统的快速发展不断提高系统的自主能力，其安全认证变得极其重要。虽然用于自主控制设计的安全强化学习（RL）算法的最新进展令人鼓舞，但这些算法是负责任的。然而，许多系统必须在不可预测的环境中运行，在这些环境中，安全和性能规范可能会出现危险的差异。此外，RL 代理必须在现实条件下进行学习。当前的强化学习实践假设可以提供丰富且高质量的数据，并且可以完全观察整个系统的状态。该奖项支持创建低复杂性安全学习算法的研究。对于配备高效冲突管理机制以安全地提供尽可能多的性能的部分可观察系统，进步将对自主系统、机器人、制造、智能电网等应用产生广泛影响。该研究项目旨在开发。该项目的目标有两个：1）提出直接数据驱动的学习方法，用于部分可观测非线性系统中的备份安全控制策略。利用 L-额外样本动力学、概率收缩性和凸提升等概念，将能够仅使用测量的噪声来学习具有非凸安全集的非线性系统的安全控制策略。 2) 引入新颖的合并方法，通过将学习的备份安全控制策略与支持学习的控制策略合并来主动管理冲突，这些方法将实现主动的冲突管理，以避免冲突发生。对于冲突管理，强化学习智能体的水平集将根据情况进行调整，使智能体与安全约束保持一致，即学习安全形价值函数，以通过考虑安全性和安全性来有效解决冲突。最优性该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。