CDS&E-MSS/Collaborative Research: Sequential Design for Stochastic Control: Active Learning of Optimal Policies

CDS

• 批准号: 1521702
• 负责人: Robert Gramacy
• 金额: $ 22.85万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521702&HistoricalAwards=false
• 关键词: CDS& MSS; Collaborative; Research; Sequential

This research project aims to build new cross-disciplinary algorithms that blend concepts from applied probability, control, and statistical modeling to tackle computational challenges in large-scale optimization. Creation of such new links is another step in building a next-generation of high-performance algorithms needed to meet the increasingly complex problems arising in applications as diverse as finance, energy storage and security, and the management of epidemics. The project's research agenda is grounded in two concrete application areas where it is crucial to tackle industrial-grade high-fidelity models. One is the efficient management of cycled commodity assets, including gas storage, battery storage, or fleets of power plants as energy infrastructure is transitioned to the "smart grid." A second is timely and effective response to unfolding infectious disease outbreaks, notably influenza. Both present major cross-disciplinary challenges. We see vast potential for algorithms which expand capabilities for aspects of quantitative control, and thus provide higher quality information to decision makers. Our goal is to produce a smarter, more targeted, use of random numbers in a new wave of lean stochastic solvers, and subsequently an expansion of the size of problems that can be tackled with existing computing capabilities. The educational core of the project contributes to inter-disciplinary training in mathematical sciences across undergraduate, graduate and postdoctoral levels. The collaborative initiatives will also enhance the research infrastructure through exchange of ideas between the two campuses (Univesrity of California-Santa Barbara and University of Chicago) and communities of statisticians, operations researchers and engineers. All algorithms would be documented and publicly released to the wider scientific community. Deployment of simulation based schemes remains key for control of stochastic systems that require realistic high-fidelity representations. This project will develop new Monte Carlo algorithms for a class of stochastic control problems by erecting novel bridges between dynamic control and methods of sequential design and statistical learning. Our research agenda hinges on sequential, active learning of optimal action sets, so that the algorithms adaptively allocate computing resources to better enhance fidelity of the approximated control strategies. Such targeted use of Monte Carlo simulations links approximate dynamic programming with response surface modeling, marrying two so-far disparate areas of applied mathematics and statistics. The resulting adaptive schemes will facilitate orders of magnitude savings in simulation budgets, expanding the frontier for predictive modeling and decision making under uncertainty. The proposed research will advance the theory of algorithms for dynamic control over massive multi-dimensional state spaces, where curses of dimensionality are unavoidable. Simultaneously, integration of the statistical and computational theories in this direction will open new lines of interdisciplinary quantitative research. Through enhancing knowledge discovery in large-scale control settings, the projects will facilitate transition to practice in novel contexts. With the aim of reaching out to diverse users from the mathematical, biological, physical and engineering sciences, producing general purpose open-source software via R packages is a primary deliverable of the project, and will be supplemented by a database of case studies.

该研究项目旨在构建新的跨学科算法，融合应用概率，控制和统计建模的概念，以解决大规模优化中的计算挑战。创建这样的新链接是构建下一代高性能算法的又一步，这些算法需要满足金融、能源存储和安全以及流行病管理等各种应用中出现的日益复杂的问题。该项目的研究议程基于两个具体的应用领域，其中解决工业级高保真模型至关重要。一个是循环商品资产的有效管理，包括天然气储存、电池储存或发电厂车队，因为能源基础设施正在向“智能电网”过渡。“第二是及时有效地应对正在爆发的传染病疫情，特别是流感。两者都提出了重大的跨学科挑战。我们看到了算法的巨大潜力，这些算法扩展了定量控制方面的能力，从而为决策者提供了更高质量的信息。我们的目标是在新一波的精益随机求解器中产生更智能，更有针对性的随机数使用，并随后扩大现有计算能力可以解决的问题的规模。该项目的教育核心有助于跨本科，研究生和博士后水平的数学科学的跨学科培训。合作举措还将通过两个校区（加利福尼亚大学圣巴巴拉分校和芝加哥）以及统计学家、业务研究人员和工程师社区之间的思想交流，加强研究基础设施。所有算法都将被记录在案，并向更广泛的科学界公开发布。部署基于仿真的计划仍然是控制随机系统，需要现实的高保真度表示的关键。本项目将通过在动态控制与顺序设计和统计学习方法之间架起新的桥梁，为一类随机控制问题开发新的蒙特卡罗算法。我们的研究议程取决于顺序，主动学习的最优动作集，使算法自适应地分配计算资源，以更好地提高近似控制策略的保真度。这样有针对性地使用蒙特卡罗模拟链接近似动态规划与响应面建模，结合两个迄今为止完全不同的应用数学和统计学领域。由此产生的自适应方案将有助于节省模拟预算的数量级，扩大预测建模和决策的不确定性的前沿。本文的研究成果将推动大规模多维状态空间的动态控制算法理论的发展，而多维状态空间的维数灾难是不可避免的。同时，统计和计算理论在这一方向的整合将开辟跨学科定量研究的新领域。通过加强大规模控制环境中的知识发现，这些项目将促进在新环境中向实践的过渡。为了接触来自数学、生物、物理和工程科学的不同用户，通过R软件包制作通用开放源码软件是该项目的主要交付成果，并将辅之以案例研究数据库。