基于课程掩码的通用技能学习
Learning Versatile Skills with Curriculum Masking
AI总结：
针对离线强化学习掩码预测预训练中不同复杂度技能学习的平衡难题，提出课程掩码预训练范式CurrMask，通过动态调整掩码方案逐步习得多样技能，在零样本任务和微调任务上均表现优异。
摘要：
掩码预测凭借其灵活的掩码方案，已成为离线强化学习（RL）中极具前景的预训练范式，能够通过统一模型在各类下游任务上实现灵活推理。尽管掩码预测具备通用性，但如何平衡不同复杂度水平的技能学习仍不明确。为解决这一问题，我们提出CurrMask——一种面向序列决策的课程掩码预训练范式。受人类通过课程组织知识进行学习的机制启发，CurrMask在预训练过程中调整掩码方案以学习通用技能。通过大量实验，我们证明CurrMask在技能提示任务、目标条件规划任务上展现出优异的零样本性能，在离线RL任务上的微调性能也具备竞争力。此外，我们对训练动态的分析表明，CurrMask通过动态调整掩码方案，能够逐步习得不同复杂度的技能。
英文摘要：
Masked prediction has emerged as a promising pretraining paradigm in offline reinforcement learning (RL) due to its versatile masking schemes, enabling flexible inference across various downstream tasks with a unified model. Despite the versatility of masked prediction, it remains unclear how to balance the learning of skills at different levels of complexity. To address this, we propose CurrMask, a curriculum masking pretraining paradigm for sequential decision making. Motivated by how humans learn by organizing knowledge in a curriculum, CurrMask adjusts its masking scheme during pretraining for learning versatile skills. Through extensive experiments, we show that CurrMask exhibits superior zero-shot performance on skill prompting tasks, goal-conditioned planning tasks, and competitive finetuning performance on offline RL tasks. Additionally, our analysis of training dynamics reveals that CurrMask gradually acquires skills of varying complexity by dynamically adjusting its masking scheme.
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。
