Adaptive Dynamic Programming Control
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.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.