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CAREER: Lyapunov Drift Methods for Stochastic Recursions: Applications in Cloud Computing and Reinforcement Learning

职业：随机递归的李亚普诺夫漂移方法：云计算和强化学习中的应用

基本信息

批准号：
2144316
负责人：
Siva Theja Maguluri
金额：
$ 50万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144316&HistoricalAwards=false
关键词：
CAREER Lyapunov Drift Methods Stochastic

项目摘要

Part I:The ongoing Artificial Intelligence revolution is possible due to progresses in two distinct areas. The first is the development of novel algorithms in machine learning paradigms such as Reinforcement Learning, that overcome long-standing challenges; the second is the breakthroughs in cloud computing infrastructure based on large data centers that enables one to collect, store and process large amounts of data very easily and at a short notice. In spite of tremendous success stories in both these areas, fundamental trade-offs and optimal performance is not understand and theory lags far behind practice. In spite of seeming to be very distinct problems, both Reinforcement Learning and Cloud computing can be studied using stochastic recursions. The goal of this CAREER project is to take a unified theoretical viewpoint of both these seemingly distinct areas first developing a general theory of stochastic recursions, and then to use it to study both Reinforcement Learning and Cloud computing. In particular, we will use the theory to develop novel learning algorithms with provably optimal sample complexity across various paradigms such as off-policy learning and actor-critic framework. The theory of stochastic recursions as well as the novel learning algorithms will also be used to develop optimal scheduling algorithms for cloud computing data centers that minimize the tail of delay experienced by the users. The novel algorithms developed during the course of this project will be implemented through collaborations with partners in industry as well as at Georgia Tech’s internal cloud. A Jupyter based open source RL simulation platform will be developed, and the novel algorithms developed during the course of this project will be included in this platform. The platform is used not only in dissemination of the outcome of this project, but also for undergraduate research projects, course projects for a new course on Reinforcement learning, and for STEM outreach activities to K-12 education. In addition to dissemination of research results through conferences and journal publications, we will develop a novel special topics course, and bring out a monograph on the unified Lyapunov framework for stochastic recursions. In addition, training of graduate and undergraduate students forms a core part of the project with special emphasis on mentoring future faculty. Part 2: Intellectual Merit:The proposed work is organized into three interdependent thrusts. Thrust I builds a Lyapunov theory of stochastic recursions, where we obtain finite-time mean square error and exponential tail bounds, as well as characterize the steady-state limiting distribution for a broad class of stochastic recursions. This thrust forms the foundation for the next two thrusts.Thrust II studies the finite-time mean-square bounds, tail probability bounds (aka PAC bounds), sample complexity, and steady-state behavior of RL algorithms under three paradigms, viz., off-policy RL, two time-scale policy space algorithms (such as actor-critic) and average reward RL, and develops novel, fast, RL algorithms with near optimal sample efficiency. Thrust-III studies scheduling problems in data center networks, with the goal of minimizing mean delay and delay tails. Using the Lyapunov theory from Thrust I, we develop novel low complexity algorithms with provable guarantees on steady-state delay in the heavy-traffic asymptotic regime. With these as initial policies, we will deploy RL algorithms from Thrust II to learn new scheduling policies that are optimal even in the preasymptotic regime, which is of practical interest. All the proposed algorithms will be evaluated using real world traffic traces through our collaborations with industry partners. Broader Impacts:The proposed work, and the PI’s ongoing industry collaborations have potential for significant societal impact by making RL and cloud computing more efficient. The proposed Lyapunov theory for Stochastic Recursions is applicable in many other disciplines. And so, the PI will disseminate it widely through a special topics course, a monograph, and tutorials, in addition to conference and journal publications. The project integrates research with educational activities at every level. A Jupyter based RL simulation platform and a library of notebooks that we will build, will serve as an extensive pedagogical resource for these activities. The PI will continue his ongoing involvement in undergraduate research through the REU program and the VIP program at Georgia Tech. In order to fulfill a growing demand, the PI will develop a new interdisciplinary undergraduate level RL course and extensively use the RL simulation platform. To promote STEM activities, the PI will take part in outreach activities to local high schools working with an academic professional in ISyE and will mentor high school teachers through the GIFT program. To support Ph.D. students interested in academic career, the PI runs a future faculty mentorship program. The PI is committed to broadening participation, and currently advises a female Hispanic student, and has advised several URM undergraduate students.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.

第一部分：由于两个不同领域的进展，正在进行的人工智能革命是可能的。首先是机器学习范式(如强化学习)中新算法的开发，以克服长期存在的挑战；其次是基于大型数据中心的云计算基础设施的突破，使人们能够非常轻松地和在短时间内收集、存储和处理大量数据。尽管在这两个领域都有巨大的成功案例，但人们对基本的权衡和最佳绩效并不了解，理论远远落后于实践。尽管强化学习和云计算似乎是非常不同的问题，但它们都可以使用随机递归进行研究。这个职业项目的目标是对这两个看似不同的领域采取统一的理论观点，首先开发随机递归的一般理论，然后使用它来研究强化学习和云计算。特别是，我们将使用该理论来开发新的学习算法，该算法具有可证明是最优的样本复杂性，跨越各种范式，如非策略学习和行为者-批评者框架。随机递归理论以及新的学习算法也将被用来开发云计算数据中心的优化调度算法，使用户体验到的延迟尾巴最小化。在该项目过程中开发的新算法将通过与行业合作伙伴以及佐治亚理工学院的内部云合作来实施。将开发一个基于Jupyter的开源RL仿真平台，该平台将包含在该项目过程中开发的新算法。该平台不仅用于传播这一项目的成果，还用于本科生研究项目、关于强化学习的新课程的课程项目以及STEM针对K-12教育的外联活动。除了通过会议和期刊出版物传播研究成果外，我们还将开发一个新的专题课程，并出版一本关于随机递归的统一Lyapunov框架的专著。此外，研究生和本科生的培训是该项目的核心部分，特别强调对未来教师的指导。第2部分：智力价值：拟议的工作被组织成三个相互依赖的推动力。推力I建立了随机递推的Lyapunov理论，得到了有限时间均方误差和指数尾界，并刻画了一大类随机递推的稳态极限分布。Thrust II研究了RL算法在非策略RL、两时间尺度策略空间算法(如参与者-批评者)和平均奖励RL三种范型下的有限时间均方界、尾概率界(又名PAC界)、样本复杂度和稳态行为，并开发了具有接近最优样本效率的新颖、快速的RL算法。推力-III研究数据中心网络中的调度问题，目标是最小化平均延迟和延迟拖尾。利用推力I中的Lyapunov理论，我们提出了一种新的低复杂度算法，该算法在大流量渐近状态下具有可证明的稳态时延保证。以这些策略为初始策略，我们将使用来自推力II的RL算法来学习新的调度策略，这些策略即使在预渐近状态下也是最优的，这具有实际意义。所有建议的算法都将通过我们与行业合作伙伴的合作，使用真实世界的流量跟踪进行评估。更广泛的影响：拟议的工作，以及PI正在进行的行业合作，通过提高RL和云计算的效率，有可能产生重大的社会影响。所提出的随机递归的Lyapunov理论也适用于许多其他学科。因此，除了会议和期刊出版物外，PI还将通过专题课程、专著和教程广泛传播它。该项目将研究与各级教育活动相结合。我们将建立一个基于Jupyter的RL模拟平台和一个笔记本库，作为这些活动的广泛教学资源。PI将通过REU项目和佐治亚理工学院的VIP项目继续参与本科生研究。为了满足日益增长的需求，PI将开发一门新的跨学科本科层次的RL课程，并广泛使用RL仿真平台。为了促进STEM活动，PI将参加与ISyE学术专业人员合作的当地高中的外联活动，并将通过GIFT计划指导高中教师。为了支持对学术事业感兴趣的博士生，国际学生联合会开展了一个未来的教师指导计划。PI致力于扩大参与，目前为一名西班牙裔女性学生提供建议，并为几名URM本科生提供建议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。