Temel Kavramlar
The authors rigorously analyze the stability and convergence properties of the value function QL associated with Lipschitz continuous optimal control problems, and leverage these insights to propose a new HJB-based reinforcement learning algorithm.
Özet
The paper addresses the stability and convergence properties of the value function QL in Lipschitz continuous optimal control problems, which is crucial for the development of effective reinforcement learning algorithms in continuous-time settings.
Key highlights:
- The authors establish that QL is uniformly Lipschitz continuous in both the state and action variables, and derive quantitative estimates on the rate of change of QL with respect to the Lipschitz constraint parameter L.
- They prove that QL converges to the value function Q of the classical optimal control problem as L goes to infinity, and provide a rate of convergence under additional structural assumptions on the dynamics and reward functions.
- The authors introduce a generalized framework for Lipschitz continuous control problems that incorporates the original problem, and leverage this to propose a new HJB-based reinforcement learning algorithm.
- The stability properties and performance of the proposed method are evaluated on well-known benchmark examples and compared to existing approaches.
İstatistikler
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