The proposed work aims to extend results in reinforcement learning theory to dynamic game problems relevant to output feedback robust nonlinear control. There are two primary motivations for this: (a).To develop schemes to overcome the prohibitive computational cost encountered while designing and implementing robust nonlinear controllers. (b).Employ the dynamic game framework as a stepping stone leading to the development of an analytical machinery suitable for posing, and solving intelligent control problems. The former is concerned primarily with off-line schemes for approximating the key equations, and development of techniques to efficiently compute and represent the control policy. The latter is concerned with on-line schemes, where one needs to integrate identification, control, and the ability to improve performance in finite amount of time1 with finite computational resources. The latter has less available information on system model and environment; thus learning is an essential component of the methodology. With these objectives in mind, special emphasis needs to be placed on obtaining algorithms that exhibit good finite time performance, and do so with finite amount of resources (computational). Furthermore, in order to efficiently integrate the components of the resulting architectures, one needs to also develop (finite time) performance bounds for these algorithms. The approach calls for first studying the problem in the context of finite state automata, and then extending the results to discrete time dynamical system models. The proposed work intends to study: (a).Extensions of reinforcement learning to obtain finite time performance bounds. (b).Development of schemes to directly identify the information most relevant for control (information state), and to do so with specified accuracy in a finite amount of time. This calls for the development of measures of risk to tradeoff exploration and control for on-line implementation. (c)Model structures in. (b) that lead to reduction in complexity, and lend themselves to efficient learning. (d).Extension of the current analytical framework for studying reinforcement learning to account for the unpredictability associated with intelligence. (e).Exploiting the relationship between risk-sensitive control and dynamic games to harness the structure offered by probability theory. (f).Development of architectures, and software that efflciently implement the algorithms obtained. Results obtained from this research project, coupled with the development of appropriate complexity metrics would result in a framework for posing, and analyzing a wide variety of intelligent control problems. Such an approach would lead to controllers that are inherently robust, yet capable of adapting their behaviour to perceived changes in the system/environment. The results would be applicable to computation and implementation of robust nonlinear control at one end, to truly autonomous control for large, complex systems at the other. Specific applicatlon domains include chemical process control, semiconductor manufacturing, and control of large communication networks. ***
