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About this paper
Michael Muehlebach, Zhiyu He, Michael I. Jordan
Researchers at the Max Planck Institute for Intelligent Systems and Inria Paris introduce a multi-model framework for online reinforcement learning in continuous state-action spaces with nonlinear dynamics and non-episodic settings. The framework provides frequentist policy-regret guarantees across finite, general, and parametrized model classes, while ensuring closed-loop stability.
Key insights
- A multi-model perspective, integrating elements from Hedge-type updates and posterior sampling, effectively addresses the exploration-exploitation dilemma in continuous nonlinear systems.
- The explicit addition of excitation during online learning is crucial for satisfying persistence of excitation conditions and enabling rapid, provable model identification.
- By leveraging Lyapunov stability analysis and concentration inequalities, sublinear policy regret and uniform boundedness of state trajectories can be guaranteed for broad classes of nonlinear systems.
Summary by alphaXiv.
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