This cluster of papers focuses on the application of Adaptive Dynamic Programming and Reinforcement Learning techniques to solve optimal control problems in continuous-time nonlinear systems. It explores the use of neural networks, policy iteration, actor-critic algorithms, and $H_{infty}$ control for online learning and feedback control in various domains such as robotics, energy management, and multi-agent systems.