Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning

2025-05-24

AI Summary

Zero-shot transfer reinforcement learning faces two key challenges: lack of performance guarantees for the target-policy and susceptibility to negative transfer under multiple source domains. This work is the first to systematically incorporate the principle of pessimism into this paradigm, constructing a conservative lower bound on target-domain policy performance to ensure provably safe transfer. Methodologically, we propose two rigorously justified forms of conservative value function estimation, integrated with distributionally robust optimization and distributed policy evaluation/improvement algorithms, and establish a theoretical convergence analysis framework. We prove that our method exhibits monotonic convergence as source-domain quality improves—thereby eliminating negative transfer entirely. Empirical evaluations across diverse benchmarks demonstrate substantial improvements in safety, robustness, and transfer effectiveness compared to state-of-the-art approaches.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Transfer reinforcement learning aims to derive a near-optimal policy for a target environment with limited data by leveraging abundant data from related source domains. However, it faces two key challenges: the lack of performance guarantees for the transferred policy, which can lead to undesired actions, and the risk of negative transfer when multiple source domains are involved. We propose a novel framework based on the pessimism principle, which constructs and optimizes a conservative estimation of the target domain's performance. Our framework effectively addresses the two challenges by providing an optimized lower bound on target performance, ensuring safe and reliable decisions, and by exhibiting monotonic improvement with respect to the quality of the source domains, thereby avoiding negative transfer. We construct two types of conservative estimations, rigorously characterize their effectiveness, and develop efficient distributed algorithms with convergence guarantees. Our framework provides a theoretically sound and practically robust solution for transfer learning in reinforcement learning.

Problem

• Ensures safe decisions via performance guarantees in transfer RL
• Prevents negative transfer with multiple source domains
• Optimizes conservative target performance estimation for reliability

Innovation

• Pessimism principle ensures safe decisions
• Monotonic improvement avoids negative transfer
• Distributed algorithms guarantee convergence