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.  ABOUT NDSS The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.  Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.  Permalink

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TrajDeleter is a novel approach for machine unlearning in offline reinforcement learning agents, allowing specific training trajectories to be removed from trained models without full retraining. The method works by degrading agent performance on states from unwanted trajectories while maintaining performance on remaining data. Testing across six RL algorithms shows TrajDeleter requires only 1.5% of retraining time while successfully unlearning 94.8% of targeted trajectories. The research addresses growing privacy and data governance needs in RL applications like healthcare and energy management.

NDSS 2025 – TrajDeleter: Enabling Trajectory Forgetting In Offline Reinforcement Learning Agents