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.

本研究的目的是为在通信图拓扑上交互的复杂人类工程系统设计多人游戏中的决策和控制新算法。标准微分游戏解决方案适用于具有单一动态和多个动作输入的系统。然而，现实系统由具有自己的个体动态的代理组成，并且仅与社交图拓扑中的直接邻居进行交互。该方法旨在将神经生物学学习、具有代理之间局部交互的社会生物学系统以及多人微分博弈方面的发现结合起来，为非线性动力系统开发新颖的反馈控制结构。智力优点。标准微分博弈论解决方案通常是离线设计方法，依赖于求解需要完整系统动力学知识的非线性设计方程。基于强化学习的近似动态规划技术将用于开发新颖的自适应控制结构，该结构可以使用沿系统轨迹测量的数据实时在线学习博弈论解决方案，而无需了解完整的系统动力学。将寻求不同博弈均衡定义的解决方案，包括纳什、帕累托、纳什讨价还价和合作博弈。更广泛的影响。该研究将强化学习、博弈论和微分动力系统结合在一起，有助于弥合计算智能和控制系统社区之间的差距。将应用于风能、太阳能等可再生能源分布式电力微电网的协同控制。 UTA 现有的项目将扩展到女性工程、美国学生研究、高中工程技术和 K-12 外展。