PAC-Bayesian Reinforcement Learning Trains Generalizable Policies 事件

PAC-Bayesian Reinforcement Learning Trains Generalizable Policies arXiv:2510.10544v3 Announce Type: replace-cross Abstract: We derive a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for Markov dependencies in the data, through the chain's mixing time. This contributes to overcoming challenges in obtaining generalization guarantees for reinforcement learning, where the sequential nature of data breaks the independence assumptions underlying classical