Provable Zero-Shot Generalization in Offline Reinforcement Learning

2025-03-11

AI Summary

This paper investigates zero-shot generalization in offline reinforcement learning: learning a policy from multi-environment offline datasets that performs efficiently in unseen test environments without any online interaction. We establish the first formal theoretical framework for this problem and propose two algorithms—Pessimistic Empirical Risk Minimization (PERM) and Pessimistic Proximal Policy Optimization (PPPO). PERM integrates conservative policy evaluation with empirical risk minimization, while PPPO incorporates pessimistic constraints directly into the proximal policy optimization pipeline. We provide rigorous theoretical guarantees showing that both algorithms converge to near-optimal policies across environments, ensuring provable zero-shot generalization. This work delivers the first theoretically grounded foundation for generalization in offline RL, overcoming key limitations of prior approaches—which either rely on environment-specific assumptions or require online fine-tuning.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Offline RL with zero-shot generalization to unseen environments
• Classical offline RL fails in new environments
• Propose PERM and PPPO for better generalization

Innovation

• Pessimistic empirical risk minimization for offline RL
• Pessimistic proximal policy optimization for ZSG
• Leveraging pessimistic evaluation to enhance generalization