NDSS 2025 – TrajDeleter: Enabling Trajectory Forgetting In Offline Reinforcement Learning Agents
好的,我现在需要帮用户总结一篇文章的内容,控制在100字以内。首先,我得仔细阅读用户提供的文章内容。 文章主要讲的是机器学习中的“轨迹遗忘”问题,特别是在离线强化学习中的应用。作者们提出了一个叫做TrajDeleter的方法,用于让强化学习代理能够快速且完全地删除特定轨迹的影响。同时,他们还开发了TrajAuditor来评估这种方法的效果。 接下来,我需要提取关键信息:研究对象是离线强化学习代理,方法是TrajDeleter和TrajAuditor,实验结果表明该方法高效且有效。此外,文章提到这个方法在实际应用中表现良好,并且所需时间比重新训练少很多。 现在,我需要用简洁的语言把这些要点浓缩到100字以内。要确保涵盖研究目的、方法、结果和效率。同时,避免使用复杂的术语,保持语言流畅自然。 最后,检查字数是否符合要求,并确保信息准确无误。 </think> 本文介绍了一种名为TrajDeleter的方法,用于使离线强化学习代理能够快速删除特定轨迹的影响。该方法通过引导代理在遇到与遗忘轨迹相关联的状态时表现恶化,同时保持对其他轨迹的性能。实验表明,该方法只需重新训练时间的1.5%,即可有效遗忘94.8%的目标轨迹,并在实际环境中保持良好性能。 2026-1-29 16:0:0 Author: securityboulevard.com(查看原文) 阅读量:0 收藏

Session 10D: Machine Unlearning

Authors, Creators & Presenters: hen Gong (University of Vriginia), Kecen Li (Chinese Academy of Sciences), Jin Yao (University of Virginia), Tianhao Wang (University of Virginia)
PAPER
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
Reinforcement learning (RL) trains an agent from experiences interacting with the environment. In scenarios where online interactions are impractical, offline RL, which trains the agent using pre-collected datasets, has become popular. While this new paradigm presents remarkable effectiveness across various real-world domains, like healthcare and energy management, there is a growing demand to enable agents to rapidly and completely eliminate the influence of specific trajectories from both the training dataset and the trained agents. To meet this problem, this paper advocates TRAJDELETER, the first practical approach to trajectory unlearning for offline RL agents. The key idea of TRAJDELETER is to guide the agent to demonstrate deteriorating performance when it encounters states associated with unlearning trajectories. Simultaneously, it ensures the agent maintains its original performance level when facing other remaining trajectories. Additionally, we introduce TRAJAUDITOR, a simple yet efficient method to evaluate whether TRAJDELETER successfully eliminates the specific trajectories of influence from the offline RL agent. Extensive experiments conducted on six offline RL algorithms and three tasks demonstrate that TRAJDELETER requires only about 1.5% of the time needed for retraining from scratch. It effectively unlearns an average of 94.8% of the targeted trajectories yet still performs well in actual environment interactions after unlearning. The replication package and agent parameters are available.
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文章来源: https://securityboulevard.com/2026/01/ndss-2025-trajdeleter-enabling-trajectory-forgetting-in-offline-reinforcement-learning-agents/
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