Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Meanwhile, function approximation addresses continuous state spaces but typically weakens convergence guarantees. In this work, we develop a new algorithm that combines the strengths of Kernel-Based Reinforcement Learning, which features instance-based state representation and kernel-based function approximation, and Prioritized Sweeping, which features model-based exploration. The resulting algorithm, Kernel-Based Prioritized Sweeping, empirically converges to good policies in continuous domains with relatively small amounts of data.
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