Cluster-Based Social Reinforcement Learning
This work addresses the problem of modeling agent dependencies in social RL for applications like fake news mitigation and personalized services, offering an incremental improvement over existing methods.
The paper tackles the challenge of efficiently incorporating inter-agent dependencies in social reinforcement learning for large networks by proposing a cluster-based approach with dynamic clustering, resulting in more accurate policy estimates and faster convergence compared to baselines.
Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous social RL approaches either ignore agents dependencies or model them in a computationally intensive manner. In this work, we incorporate agent dependencies efficiently in a compact model by clustering users (based on their payoff and contribution to the goal) and combine this with a method to easily derive personalized agent-level policies from cluster-level policies. We also propose a dynamic clustering approach that captures changing user behavior. Experiments on real-world datasets illustrate that our proposed approach learns more accurate policy estimates and converges more quickly, compared to several baselines that do not use agent correlations or only use static clusters.