Novel Algorithms to Approximate the Future Consequence of Sequential Decisions

近似连续决策的未来后果的新算法

• 批准号: RGPIN-2017-04877
• 负责人: SabouriBaghAbbas, Alireza
• 金额: $ 1.46万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636717
• 关键词: Novel; Algorithms; Approximate; Future; Consequence

Many complex problems arising in business, health care, and transportation can be modelled as sequential decision making problems under uncertainty, meaning that a decision maker has to make decisions periodically while some random events unfold over time. For instance, an airline dynamically changes the fare for different flights over a network of cities without knowing the actual future demand, trying to maximize its revenue while managing the risk of unsold seats. These problems can be conveniently modelled in the form of dynamic programs, a method that finds the best decision by maximizing the sum of immediate reward and the expected future reward. Unfortunately, for many practical problems, the number of future scenarios that one should consider in order to calculate the expected future reward function is exponentially large, making exact calculation of this function intractable. In order to overcome this issue, approximate dynamic programming (ADP) methods have been developed to find an approximate optimal solution.

商业、医疗保健和交通运输中出现的许多复杂问题可以建模为不确定性下的顺序决策问题，这意味着决策者必须在一些随机事件随着时间的推移而发生的同时定期做出决策。例如，一家航空公司在不知道未来实际需求的情况下动态改变城市网络中不同航班的票价，试图在管理未售出座位的风险的同时最大化其收入。这些问题可以方便地以动态程序的形式建模，动态程序是一种通过最大化即时奖励和预期未来奖励之和来找到最佳决策的方法。不幸的是，对于许多实际问题，为了计算预期的未来奖励函数而应该考虑的未来场景的数量呈指数级增长，使得该函数的精确计算变得困难。为了克服这个问题，已经开发了近似动态规划（ADP）方法来寻找近似最优解。