Ryan Horache

PAC-Bayesian Reinforcement Learning Trains Generalizable Policies

2025 · arXiv (Cornell University)

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 …