New theory defines optimal regret for kernel-based reinforcement learning

Researchers have developed a new theoretical framework for understanding minimax regret in kernel-based reinforcement learning. The study focuses on scenarios with continuous state and action spaces and adversarial initial states, establishing optimal regret rates dependent on Sobolev smoothness parameters and state-action dimensions. The proposed algorithm, Sobolev Optimal Value Iteration (SOVI), achieves these rates by combining local polynomial approximation with the shared RKHS structure, addressing an open problem in the field. AI

IMPACT Establishes theoretical bounds for kernel-based RL, potentially guiding future algorithm development in complex environments.

RANK_REASON The cluster contains a research paper detailing theoretical advancements in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• 2024
• arXiv
• Hugging Face
• Kernel-Based Reinforcement Learning
• Minimax Regret for Sobolev Kernel-Based Reinforcement Learning
• reproducing kernel Hilbert space
• Sobolev
• Sobolev Optimal Value Iteration
• Vakili

• paper
• other

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