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

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的多人游戏中的决策和控制设计新的算法。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，现实系统是由具有自己的个体动态的代理组成的，并且在社会图拓扑中只与它们的近邻交互。该方法是将神经生物学学习、具有代理之间局部相互作用的社会生物学系统和多玩家微分博弈中的发现结合起来，为非线性动态系统开发新的反馈控制结构。知识价值。标准微分博弈论解决方案通常是离线设计方法，依赖于解决非线性设计方程，需要全系统动力学知识。基于强化学习的近似动态规划技术将用于开发新的自适应控制结构，该结构使用沿系统轨迹测量的数据在线实时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、纳什议价和合作博弈。更广泛的影响。该研究将通过将强化学习、博弈论和微分动力系统结合在一起，帮助弥合计算智能和控制系统社区之间的差距。将应用于风能和太阳能等可再生能源的分布式电力微电网的合作控制。UTA现有的项目将扩大到女性工程、美国学生研究、高中工程技术和K-12推广。

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.