Reinforcement learning-based optimal control for stochastic opinion dynamics.

Area of Science:

• * Complex Systems Science
• * Network Science
• * Control Theory

Background:

• * Understanding and influencing opinion dynamics in social networks is crucial for applications ranging from marketing to public policy.
• * Existing control methods often struggle with the inherent stochasticity and unknown dynamics of real-world social systems.
• * Bridging model-based and data-driven approaches is essential for robust opinion control.

Purpose of the Study:

• * To propose an integrated framework for optimal control of opinion dynamics in social networks.
• * To address three scenarios: model-based stochastic control, model-free reinforcement learning (RL), and data-driven RL for unknown systems.
• * To develop a novel RL control framework leveraging convex quadratic optimization.

Main Methods:

• * Model-based stochastic control for systems with known probability distributions.
• * Model-free reinforcement learning (RL) for systems with unknown interaction distributions.
• * Data-driven RL for systems with fully unknown, time-varying network dynamics.
• * Convex quadratic optimization integrated within the RL framework.

Main Results:

• * The proposed framework effectively manages opinion dynamics across all three addressed scenarios.
• * The integration of RL with convex optimization bridges model-based and data-driven control paradigms.
• * Numerical simulations validate the framework's capability in network manipulation and coordination tasks.

Conclusions:

• * The developed integrated framework provides a versatile approach to optimal control of opinion dynamics.
• * This research offers new theoretical insights and practical tools for social network manipulation and multi-agent coordination.
• * The findings highlight the potential of data-driven learning methods in complex dynamic systems.