Optimization and Performance Evaluation of Network Models

拟议的研究将考虑复杂系统的最优控制、强化学习和性能评估的最新方法的发展。所采用的方法是基于PI最近在马尔可夫链稳定性理论、线性规划方法和流体模型近似方面所做的工作。动态优化的研究将基于PI的最新发现，PI建立了随机模型的最优控制和更简单的‘漏桶’流体模型的最优控制之间的关系。通过深入分析线性规划方法来逼近感兴趣的最优控制问题中的值函数，从而改进优化理论；对于随机模型中的策略改进，将考虑增强学习算法。PI将再次基于流体模型近似进行分析。具体应用包括开发多类排队网络的调度和路由算法。这些算法将基于流动政策的翻译、流动价值函数近似的翻译以及强化学习技术的应用。

ECS-9972957MeynThe proposed research will consider the development of recent approaches to optimal control, reinforceraent learning, and performance evaluation for complex systems. The methods to be employed are based upon recent work conducted by the PI on Markov chain stability theory, linear programming methods, and fluid model approximation.Research on dynamic optimization will be based on a recent discovery of the PI which establishes a relationship between the optimal control of a stochastic model, and the optimal control of a simpler 'leaky bucket' fluid model. The latter gives rise to a constrained L1 optimal control problem which can be solved in many practical examples.The optimization theory will be improved through a deeper analysis of linear programming methods for approximating the value function in the optimal control problem of interest.Reinforcement learning algorithms will be considered for policy improvement in stochastic models. The PI will again base the analysis on fluid model approximations.Specific applications include the development of scheduling and routing algorithms for multiclass queueing networks. The algorithms will be based upon translations of fluid policies; tranlations of fluid value function approximations; and the application of reinforcement learning techniques.