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We investigate the problem of cooperative resource allocation in multi-agent systems, focusing on dynamic scenarios such as hospital networks. In our model, agents (e.g., hospitals) aim to redistribute limited resources, such as medical personnel, in a way that satisfies both local constraints and global equity objectives. We devise a reinforcement learning approach to a dynamic scenario with time-varying resource needs. We empirically evaluate the proposed approach through extensive experiments. Our results demonstrate the effectiveness of the approach.

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Multi-agent Dynamic Resource Allocation: A Reinforcement Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Stefania Costantini,
  • Giovanni De Gasperisù,
  • Pasquale De Meo,
  • Francesco Gullo,
  • Alessandro Provetti

摘要

We investigate the problem of cooperative resource allocation in multi-agent systems, focusing on dynamic scenarios such as hospital networks. In our model, agents (e.g., hospitals) aim to redistribute limited resources, such as medical personnel, in a way that satisfies both local constraints and global equity objectives. We devise a reinforcement learning approach to a dynamic scenario with time-varying resource needs. We empirically evaluate the proposed approach through extensive experiments. Our results demonstrate the effectiveness of the approach.