Stochastic Control of Partially Observable Systems

This book explores the significance of stochastic control in partially observable systems, emphasizing the need for comprehensive theory and numerical methods, covering both linear and nonlinear filtering approaches for practical applications.

Stochastic Control of Partially Observable Systems delves into the complexities of managing systems where complete information is not available. This topic is crucial across various applications, as most real-world problems fall into this category. The book emphasizes that both deterministic control and stochastic control with full observation serve merely as approximations of reality, highlighting the necessity for a more comprehensive theory that can be effectively applied in numerical contexts.

The initial chapters of the book focus on linear theory, addressing problems that can be approached algebraically. This foundation sets the stage for understanding the principles of stochastic control in a structured manner. By exploring linear systems, the author provides readers with essential tools and methods that can be utilized in practical scenarios. This approach not only clarifies the underlying concepts but also prepares the reader for more complex discussions later in the text.

As the book progresses, it transitions into nonlinear filtering theory, which introduces the challenges of dealing with infinite-dimensional statistics. Here, the author develops various approximations and perturbation methods to tackle these complexities. This exploration is vital for those seeking to implement stochastic control strategies in real-world applications, as it equips them with the necessary theoretical background and practical techniques to address partially observable systems effectively. Overall, this book serves as a valuable resource for researchers and practitioners interested in advancing their understanding of stochastic control in uncertain environments.