Papers and Code on Adaptive Dynamic Programming (ADP)

Adaptive Dynamic Programming (ADP) is an intelligent control methodology that combines dynamic programming with function approximation techniques, primarily designed to solve optimal control problems in complex systems.

The core concept of ADP involves online learning to approximate value functions or policy functions in dynamic programming, thereby overcoming the "curse of dimensionality" that traditional dynamic programming faces in high-dimensional state spaces. It finds widespread applications in robotics control, power system optimization, and financial decision-making domains. Implementation typically requires building neural network architectures for function approximation and establishing reward mechanisms for system feedback.

Mainstream ADP approaches include: Heuristic Dynamic Programming (HDP) - Approximates value functions using neural networks Dual Heuristic Programming (DHP) - Simultaneously approximates both value functions and their gradients Global Dual Heuristic Programming (GDHP) - Integrates advantages from both previous methods Code implementations often involve separate network modules for value/policy approximation with backpropagation training algorithms.

Typical implementation framework consists of three key modules: Environment Model: Predicts state transitions (often implemented through system identification or model learning) Critic Network: Estimates long-term return value functions (using neural networks with temporal difference learning) Action Network: Generates optimal control policies (typically implemented as parameterized policy functions)

Current research focuses include: Integration with deep learning (such as deep reinforcement learning frameworks) Robustness improvements for nonlinear and uncertain systems Distributed ADP in multi-agent collaborative scenarios Modern implementations frequently utilize deep neural networks, experience replay buffers, and policy gradient methods for enhanced performance.