Goal-Oriented Reinforcement Learning Agents for Dynamic Task Allocation in Gig Economy Platforms: Integrating Behavioral Incentives and Worker Autonomy
Keywords:
reinforcement learning, gig economy, task allocation, worker autonomy, behavioral incentives, multi-agent systems, algorithmic management, fairnessAbstract
The rapid expansion of gig economy platforms has introduced unprecedented challenges in task allocation, where platform operators must balance operational efficiency with the diverse preferences and behavioral responses of autonomous workers. Traditional optimization methods, which treat workers as static resources, fail to capture the dynamic, goal-oriented nature of human decision-making. This paper proposes a goal-oriented reinforcement learning (RL) framework for dynamic task allocation that explicitly integrates behavioral incentives and worker autonomy. We argue that platforms can be modeled as multi-agent systems where each worker is a learning agent pursuing personalized goals—such as income targets, time flexibility, or skill development—while the platform acts as a meta-controller that allocates tasks to maximize collective outcomes. The framework leverages hierarchical RL and reward shaping to align platform objectives with worker intrinsic motivations, thereby reducing attrition and improving service quality. We examine the structural trade-offs between centralized efficiency and distributed autonomy, discuss the implications of deploying such systems at scale, and address concerns related to fairness, robustness, and governance. Through cross-domain comparisons with logistics, energy grids, and online labor markets, we highlight how goal-oriented RL can foster sustainable platform ecosystems. The paper concludes with policy recommendations for regulating algorithmic management in the gig economy and outlines future research directions for integrating human-in-the-loop learning with socio-technical infrastructure design.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
