Approximate Dynamic Programming Using Random Sampling

摘要提案编号：ECCS-0824077提案标题：使用随机采样的近似动态规划PI名称： 作者：Christopher G.主要研究者机构：本研究的目的是发展高性能非线性系统控制的近似动态规划方法。 该方法是使用空间局部模型的关键功能，如一个功能，代表未来的成本超过整个任务或使命（价值功能），和一个功能，告诉系统在每种情况下做什么（政策）。本论文的主要贡献在于：1）为高性能系统开发了一种基于近似动态规划的动态规划综合方法。 2)展示了如何联合收割机许多快速贪婪的本地规划器，以产生全局最优的解决方案。 3)展示了如何沿沿着显式轨迹表示知识，从而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采样创建自适应表示。 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、运输和能源等领域实现高性能（接近最优）控制。 一个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。

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.