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Dynamic Incentive Design via Reinforcement Learning Stochastic Control Optimization

Angela Li,Kevin Chen,Kun Bao,Cheng Li,Chengkun Yao,David D. Li

Published 2025 in IEEE International Conference on Systems, Man and Cybernetics

ABSTRACT

We propose a reinforcement-aware stochastic control framework for real-time reward optimization in sharing economy platforms. Unlike traditional static incentive schemes, our model dynamically allocates incentives by integrating belief-driven user behavior modeling, Nash equilibrium assumptions, and constrained utility maximization. The core framework unifies dynamic programming with reinforcement learning (RL) approximations to handle partial observability and large-scale deployment. A novel pricing-based calibration method is introduced to quantify the marginal value of a successful transaction, enabling budget-aligned incentive strategies. We further address theoretical assumptions, computational complexity, and practical implementation, providing a scalable path toward intelligent reward systems for real-world digital platforms.

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