Alberto Bernacchia

Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning

2025 · arXiv (Cornell University)

Reinforcement Learning (RL) problems are being considered under increasingly more complex structures. While tabular and linear models have been thoroughly explored, the analytical study of RL under nonlinear function approximation, especially kernel-based models, has recently …