Adaptive Dynamic Programming for Real-Time Cooperative Multi-Player Games and Graphical Games

实时协作多人游戏和图形游戏的自适应动态规划

基本信息

批准号：
1128050
负责人：
Frank Lewis
金额：
$ 27.27万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-08-01 至 2015-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1128050&HistoricalAwards=false
关键词：
Adaptive Dynamic Programming Real Time

项目摘要

The Objective of this research is to design new algorithms for decision and control in multi-player games for complex human-engineered systems interacting on communication graph topologies. Standard differential game solutions are for systems with a single dynamics and multiple action inputs. However, realistic systems are composed of agents having their own individual dynamics and only interacting with their immediate neighbors in a social graph topology. The Approach is to bring together discoveries in neurobiological learning, sociobiological systems having local interactions between agents, and multi-player differential games to develop novel feedback control structures for nonlinear dynamical systems. Intellectual Merit. Standard differential game theory solutions are generally offline design methods that rely on solving nonlinear design equations requiring knowledge of full system dynamics. Approximate Dynamic Programming techniques based on reinforcement learning will be used to develop novel adaptive control structures that learn game theory solutions online in real time using data measured along the system trajectories and without knowing full system dynamics. Solutions for different definitions of game equilibria will be sought including Nash, Pareto, Nash bargaining, and cooperative games.Broader Impacts. The research will help bridge the gap between the Computational Intelligence and the Control Systems communities by bringing together reinforcement learning, game theory, and differential dynamical systems. Applications will be made to cooperative control of distributed electric power microgrids for renewable energy such as wind and solar generation. Existing programs at UTA will be expanded in women in engineering, research for US students, high school engineering technology, and K-12 outreach.

这项研究的目的是设计新的算法，以在多游戏游戏中为复杂的人工工程系统进行决策和控制，并在通信图形拓扑上进行交互。标准差异游戏解决方案适用于具有单个动态和多个动作输入的系统。但是，现实的系统由具有自己的个人动态的代理组成，并且仅在社交图形拓扑中与直接邻居进行互动。这种方法是汇集神经生物学学习的发现，具有代理之间局部相互作用的社会生物学系统和多玩家差异游戏，以开发非线性动力学系统的新型反馈控制结构。智力优点。标准差异游戏理论解决方案通常是离线设计方法，依赖于求解需要了解完整系统动态知识的非线性设计方程。基于强化学习的近似动态编程技术将用于开发新型的自适应控制结构，这些结构使用沿系统轨迹测量的数据实时在线学习游戏理论解决方案，而不知道完整的系统动态。将寻求针对游戏平衡定义的解决方案，包括纳什，帕累托，纳什议价和合作游戏。这项研究将通过将强化学习，游戏理论和差异动态系统汇总在一起，有助于弥合计算智能与控制系统社区之间的差距。将对可再生能源（例如风能和太阳能生成）的分布式电力微电网进行合作控制。 UTA的现有计划将在工程女性，美国学生的研究，高中工程技术和K-12外展方面进行扩展。