Reinforcement Learning Algorithms Based on Dynamic Programming

本项目将研究一类基于动态规划(DP)的强化学习算法的各个方面。尽管这些算法已经被广泛研究并在许多应用中进行了试验，他们的理论还不够发达，不足以清楚地理解哪类问题可能是他们选择的方法，也不足以指导他们的应用。马萨诸塞大学的研究最近取得了相当大的进展，将这些方法与最密切相关的传统方法和了解影响他们表现的因素，有成功的也有不成功的。这些方法可能为非常大且难以分析的序列决策问题提供唯一在计算上可行的方法。本项目的目标是：1)继续发展基于DP的强化学习方法及其理论，2)研究它们的计算复杂性，以及3)定义它们最适合的问题的特征。

This project will investigate aspects of a class of reinforcement learning algorithms based on dynamic programming (DP). Although these algorithms have been widely studied and have been experimented with in many applications, their theory is not developed enough to permit a clear understanding of the classes of problems for which they may be the methods of choice, or to guide their application. Research at the University of Massachusetts has made considerable recent progress in relating these methods to the most closely related conventional methods and in understanding the factors that influence their performance, both successful and unsuccessful. These methods may provide the only computationally feasible approaches to very large and analytically intractable sequential decision problems. The objectives of this project are: 1) to continue development of DP-based reinforcement learning methods an their theory, 2) to investigate their computational complexity, and 3) to define the characteristics of problems for which they are best suited.