Improving Zero-Shot Offline RL via Behavioral Task Sampling

2026-04-28

AI Summary

This work addresses the limitation in offline zero-shot reinforcement learning where randomly sampled task vectors often fail to align with the true task distribution, thereby hindering generalization. To overcome this, the authors propose extracting implicit task vectors directly from the offline dataset, replacing conventional random sampling with a data-driven approach that better reflects the actual task distribution and refines the training objective. The method integrates task-conditioned policies, state representation learning, and offline reinforcement learning to enable efficient zero-shot adaptation to unseen reward functions. Experimental results across multiple benchmark environments demonstrate an average 20% improvement in zero-shot performance, underscoring the critical role of task sampling strategy in offline zero-shot reinforcement learning.

Abstract

Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state representations. In most existing algorithms, these task vectors are randomly sampled, implicitly assuming this adequately captures the structure of the task space. We argue that doing so leads to suboptimal zero-shot generalization. To address this limitation, we propose extracting task vectors directly from the offline dataset and using them to define the task distribution used for policy training. We introduce a simple and general reward function extraction procedure that integrates into existing offline zero-shot RL algorithms. Across multiple benchmark environments and baselines, our approach improves zero-shot performance by an average of 20%, highlighting the importance of principled task sampling in offline zero-shot RL.

Problem

• zero-shot reinforcement learning
• offline RL
• task sampling
• reward function
• generalization

Innovation

• zero-shot reinforcement learning
• offline RL
• task sampling
• reward function extraction
• behavioral task distribution