Adaptive dynamic programming (ADP), also known as approximate dynamic programming, neuro-dynamic programming, and reinforcement learning (RL), is a class of promising techniques to solve the problems of optimal control for discrete-time (DT) and continuous-time (CT) nonlinear systems.
MATLAB codes of ADPRL, including iterative and online ADPRL, are provided. Currently added ADPRL algorithms include:
- Value iteraion for DT systems
- Value iteraion for DT systems (positive semi definite initial value function)
- Policy iteration for DT systems
- Policy iteration for CT systems
- Integral reinforcement learning for partially unknown CT systems
- Model-free integral reinforcement learning for completely unknown nonaffine CT systems
- Model-free nonzero-sum games for completely unknown nonaffine CT systems
- Model-free nonzero-sum games for completely unknown nonaffine DT systems
- Online learning policy update for DT systems
- Online learning without initial admissible control for CT systems
- Parallel control-based optimal tracking for nonaffine CT systems
- MATLAB