toplogo
Resources
Sign In

Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents


Core Concepts
The core message of this paper is to propose TRAJDELETER, the first practical approach to enable offline reinforcement learning agents to rapidly and completely eliminate the influence of specific trajectories from both the training dataset and the trained agents.
Abstract
The paper introduces TRAJDELETER, a method for enabling offline reinforcement learning (RL) agents to unlearn specific trajectories from their training dataset. Offline RL trains agents using pre-collected datasets, which is useful when online interactions are impractical or risky. However, there is a growing demand to allow agents to rapidly and completely eliminate the influence of specific trajectories, for reasons such as privacy, security, or copyright. The key idea of TRAJDELETER is to guide the agent to demonstrate deteriorating performance when it encounters states associated with the unlearning trajectories, while ensuring the agent maintains its original performance level when facing other remaining trajectories. TRAJDELETER consists of two phases: Forgetting: This phase minimizes the value function Q (which estimates the expected cumulative reward) for the unlearning samples, while simultaneously maximizing Q on the remaining samples. This balances unlearning and preventing performance degradation. Convergence Training: This phase minimizes the discrepancies in cumulative rewards obtained by following the original and unlearned agents when encountering states in other remaining trajectories. This ensures the convergence of the unlearned agent. The paper also introduces TRAJAUDITOR, a simple yet efficient method to evaluate whether TRAJDELETER successfully eliminates the specific trajectories of influence from the offline RL agent. TRAJAUDITOR fine-tunes the original agent to generate shadow agents, and uses state perturbations to create diverse auditing bases, significantly reducing the time required compared to training shadow agents from scratch. Extensive experiments 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 while still performing well in actual environment interactions after unlearning, outperforming baseline methods.
Stats
TRAJDELETER requires only about 1.5% of the time needed for retraining from scratch. TRAJDELETER effectively unlearns an average of 94.8% of the targeted trajectories. The average cumulative returns show a slight difference of 2.2%, 0.9%, and 1.6% between TRAJDELETER-unlearned agents and retrained agents in the three tasks.
Quotes
"TRAJDELETER requires only about 1.5% of the time needed for retraining from scratch." "TRAJDELETER effectively unlearns an average of 94.8% of the targeted trajectories." "The average cumulative returns show a slight difference of 2.2%, 0.9%, and 1.6% between TRAJDELETER-unlearned agents and retrained agents in the three tasks."

Deeper Inquiries

How can TRAJDELETER be extended to handle unlearning of multiple sets of trajectories simultaneously

To extend TRAJDELETER to handle unlearning of multiple sets of trajectories simultaneously, we can modify the algorithm to incorporate batch processing of trajectories. Instead of focusing on unlearning one set of trajectories at a time, we can design the algorithm to handle multiple sets concurrently. This can be achieved by updating the policy and value functions for each set of trajectories in parallel, allowing the agent to forget behaviors from multiple sets simultaneously. By implementing batch processing, TRAJDELETER can efficiently unlearn multiple sets of trajectories, enhancing its scalability and effectiveness in handling complex unlearning tasks.

What are the potential limitations of TRAJDELETER in handling highly correlated or overlapping trajectories in the offline dataset

One potential limitation of TRAJDELETER in handling highly correlated or overlapping trajectories in the offline dataset is the risk of interference between the unlearning processes. When trajectories are closely related or overlap significantly, the unlearning process for one set of trajectories may inadvertently affect the unlearning of another set. This can lead to suboptimal performance and potential instability in the unlearned agent. To address this limitation, careful consideration and fine-tuning of the unlearning parameters and strategies are necessary to ensure that the agent effectively forgets specific behaviors without negatively impacting other trajectories. Additionally, incorporating advanced techniques such as trajectory clustering or prioritization based on relevance can help mitigate the challenges posed by highly correlated or overlapping trajectories.

How can the principles and techniques of TRAJDELETER be applied to enable unlearning in other machine learning domains beyond offline reinforcement learning

The principles and techniques of TRAJDELETER can be applied to enable unlearning in other machine learning domains beyond offline reinforcement learning by adapting the unlearning process to suit the specific characteristics and requirements of different domains. For example, in supervised learning, unlearning can be utilized to remove specific data points or features from a trained model to address privacy concerns or comply with data regulations. By incorporating the concepts of forgetting and convergence training, unlearning methods can be tailored to ensure the effective removal of unwanted information while maintaining the overall performance of the model. Additionally, in unsupervised learning or generative modeling, unlearning can be applied to adjust the learned representations or distributions to adapt to changing data distributions or eliminate biases. By leveraging the core principles of TRAJDELETER, such as maximizing performance on remaining data and minimizing the impact of forgotten information, unlearning techniques can be extended to various machine learning domains to enhance model adaptability and robustness.
0