SGER: Neural-Network-Based Adaptive Dynamic Programming for Maximizing Survival Probabilities

摘要提案编号：0841055提案名称：Abhijit GosaviPI机构：密苏里科技大学以前关于最优控制和自适应动态规划（ADP）的大部分工作都集中在一种最佳情况优化上，其中被控制的系统是稳定的，目标是简单地最大化收益或最小化成本。在风险敏感型ADP这一虽小但很重要的领域，该项目一直处于领先地位。ADP的目标仍然是实现利益最大化，但试图找到风险较小的策略。在这项新工作中，PI提出开发针对最坏情况的新方法，在这种情况下，稳定性或生存无法保证，优化问题是最大化生存概率。这是一类非常重要的问题。此外，他计划通过开发新的软件工具，将ADP有效地应用于离散变量和连续变量混合的问题，弥合决策、管理和风险社区与工程ADP社区之间的差距。建议的智力价值：这将是第一个将ADP（或至少是连续变量ADP）应用于一类极其重要的问题的工作。最大化生存概率的挑战是非常重要的，例如，在试图了解大脑的功能时。风险敏感ADP （RSADP）将首次应用于能够同时有效处理离散变量和连续变量的新兴ADP方法。提案的广泛影响：MatLab中针对这种优化方法的新工具可能对整个ADP领域的发展具有巨大的战略意义。这对PI和他在职业生涯的这个阶段试图开拓的领域来说，也可能比平时更有好处。这个项目的结果将为研究RSADP在工程中的许多问题奠定基础，这些问题可以使用ADP：车辆路线，结构维护（特别是在面临危险的自然灾害时），航空公司的收入管理和制造产品的供应链。

Abstract Proposal Number: 0841055Proposal Title: Neural-Network-Based Adaptive Dynamic Programming for Maximizing Survival ProbabilitiesPI Name: Abhijit GosaviPI Institution: Missouri University of Science and TechnologyMost previous work on optimal control and adaptive dynamic programming (ADP) has focused on a kind of best-case optimization, where the system to be controlled is stable and the goal is simply to maximize benefits or minimize costs. This PI has been a leader in the small but important area of risk-sensitive ADP, which still aims to maximize benefits but tries to find less risky strategies for doing so. In this new work, the PI proposes to develop new methods aimed at the worst case situation, where stability or survival can not be guaranteed, and the optimization problem is to maximize the probability of survival. This is a very important class of problems. In addition, he plans to bridge the gap between the decision, management and risk community, where he has worked so far, and the engineering ADP community, by developing new software tools that can apply ADP efficiently to problems with a mix of discrete and continuous variables.Intellectual merit of proposal: This would be the first work to apply ADP (or at least continuous-variable ADP) to an extremely important class of problems. The challenging of maximizing a probability of survival is very important, for example, in trying to understand what brains do. Risk-sensitive ADP (RSADP) will be applied for the first time to the emerging class of ADP methods capable of efficiently coping with discrete and continuous variables at the same time.Broad impact of proposal: New tools in MatLab for this kind of optimization method could be of enormous strategic importance to the development of the entire ADP field. This could also be of more than usual benefit to the PI and to the area he has tried to pioneer at this stage of his career. The results of this project will build the foundation for studying RSADP on numerous problems in engineering where ADP can be used: routing of vehicles, maintenance of structures (especially in the face of risky natural disasters), revenue management in airlines, and supply chains of manufactured products.