Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games
arXiv:1310.1384 · doi:10.1109/JAS.2014.7004681
Abstract
This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game online, without requiring persistence of excitation (PE), for a nonlinear control-affine system. Under a condition milder than PE, uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies is established.
Cited by in corpus (8)
- Concurrent learning-based approximate optimal regulation
- Model-based reinforcement learning for infinite-horizon approximate optimal tracking
- Efficient model-based reinforcement learning for approximate online optimal
- Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning
- Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of a Current
- Online Simultaneous State and Parameter Estimation for Second-order Nonlinear Systems
- Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems
- SLS-BRD: A system-level approach to seeking generalised feedback Nash equilibria