通过行为任务采样改进零样本离线强化学习
Improving Zero-Shot Offline RL via Behavioral Task Sampling
AI总结：
本文提出通过直接从离线数据集中提取任务向量来改进零样本离线强化学习，提升任务泛化能力，实验表明在多个基准环境中性能提升20%。
摘要：
本文提出通过直接从离线数据集中提取任务向量来改进零样本离线强化学习，提升任务泛化能力，实验表明在多个基准环境中性能提升20%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
