Can I read Provable Zero-Shot Generalization in Offline Reinforcement Learning on EtoBox?

Provable Zero-Shot Generalization in Offline Reinforcement Learning by Wang, Zhiyong; Yang, Chen; Lui, John C. S.; Zhou, Dongruo is a scholarly article available to read on EtoBox.

What is Provable Zero-Shot Generalization in Offline Reinforcement Learning about?

In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from different environments, and the goal of the agent is to train a policy over the training environments which performs well on test environments without further interaction. Existing work showed that classical offline RL fails to generalize to new, unseen environments. We propose pessimistic empirical risk minimization (PERM) and pessimistic proximal policy optimization (PPPO), which leverage pessimistic policy evaluation to guide policy learning and enhance generalization. We show that both PERM and PPPO are capable of finding a near-optimal policy with ZSG. Our result serves as a first step in understanding the foundation of the generalization phenomenon in offline reinforcement learning.

Author

Wang, Zhiyong; Yang, Chen; Lui, John C. S.; Zhou, Dongruo

Published

2025

Language

EN