This book summarizes innovative research results in the development of RADP theory and methods. Past literature focuses mainly on ADP analysis and design for dynamic processes without considering dynamic uncertainties. This book will first provide an overview of the well-established fields of nonlinear control, machine learning, and dynamic programming. The remaining chapters of the book will gradually develop the RADP theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. Practical applications in power systems will be covered throughout the book as illustrative examples to validate the proposed techniques. Also, one independent chapter will discuss how the developed RADP theory can be utilized as a novel computational mechanism of human movement control. The book has several outstanding features. First, the theory itself is rigorously developed with techniques from modern nonlinear control theory. Second, the book covers a variety of potential real-world applications, instead of being limited to academic numerical examples. Third, this book also covers reverse engineering topics, such as using the theory to understand the intelligence in human brains. Last but not least, the book will be self-contained in terms of proofs of theorems. For application examples, beside background information and simulation results, reusable and well commented MATLAB codes and Simulink block-sets will be provided to the users.