  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.
