Approximate Dynamic Programming Using Random Sampling

使用随机采样的近似动态规划

• 批准号: 0824077
• 负责人: Christopher Atkeson
• 金额: $ 34.82万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0824077&HistoricalAwards=false
• 关键词: Approximate; Dynamic; Programming; Using; Random

AbstractProposal Number: ECCS-0824077Proposal Title: Approximate Dynamic Programming Using Random Sampling PI Name: Atkeson, Christopher G. PI Institution: Carnegie-Mellon UniversityThe objective of this research is to develop approximate dynamicprogramming methods for the control of high performance nonlinearsystems. The approach is to use spatially local models of keyfunctions such as a function that represents future costs over anentire task or mission (the value function), and a function that tellsthe system what to do in each situation (the policy). Humanoid robotswill be used to evaluate the nonlinear control design techniques andcompare them with other approximate dynamic programming approaches.Intellectual MeritKey contributions of this work will be: 1) Developing an integratedapproach for dynamic planning based on approximate dynamic programmingfor high performance systems. 2) Showing how to combine many fastgreedy local planners to produce globally optimal solutions. 3)Showing how to represent knowledge along explicit trajectories, whichincreases the feasible sparseness of this approximate dynamicprogramming method. 4) Showing how to create an adaptiverepresentation using random sampling of states. 5) Showing how to uselocal models, and compare them to global parametric functionapproximators. 6) Showing how to use fast trajectory optimization tospeed up approximate dynamic programming.Broader impactsA specific impact of this work will be to enable high performance(near optimal) control in areas like robotics, transportation, andenergy. A more general societal payoff is industrial processes,machines, and robots that are easier to program, perform better andwaste fewer resources.

摘要提案编号：eccs -0824077提案题目：基于随机抽样PI的近似动态规划名称：Atkeson， Christopher G. PI机构：Carnegie-Mellon university本研究的目的是发展用于高性能非线性系统控制的近似动态规划方法。方法是使用关键函数的空间局部模型，例如表示整个任务或任务的未来成本的函数（价值函数），以及告诉系统在每种情况下该做什么的函数（策略）。将使用类人机器人来评估非线性控制设计技术，并将其与其他近似动态规划方法进行比较。本工作的主要贡献将是：1)开发一种基于近似动态规划的高性能系统动态规划集成方法。2)展示如何结合许多贪婪的地方规划者来产生全局最优的解决方案。3)展示了如何沿着显式轨迹表示知识，这增加了这种近似动态规划方法的可行稀疏性。4)演示如何使用状态的随机抽样创建自适应表示。5)展示如何使用局部模型，并将其与全局参数函数逼近器进行比较。6)展示了如何使用快速轨迹优化来加速近似动态规划。更广泛的影响这项工作的一个具体影响将是在机器人、交通和能源等领域实现高性能（接近最优）控制。更普遍的社会回报是工业流程、机器和机器人更容易编程，性能更好，浪费更少的资源。