Resource allocation has always been a critical challenge for construction project planning, and it affects the cost, duration, and quality of the projects. However, current methods mainly focus on a single project and lack integrated planning and optimization across a construction company's multiple projects. This paper describes a simulation of an Autonomous Resource Allocation (ARA) model using Deep Reinforcement Learning (DRL) agents and methods like Double Deep Q-Networks and combined experience replay to develop and test ARA algorithms based on data harvesting from the Internet of Things (IoT) devices. The results show that DRL can successfully perform ARA by capturing the complex interactions among resource allocation features, without needing retraining when situations change. It shows promising future possibilities for construction companies to improve resource utilization and project performance for larger and more complex construction projects.

中文翻译：

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通过基于深度强化学习的投资组合项目集成模拟自主资源分配

资源分配一直是建设项目规划的关键挑战，它影响项目的成本、工期和质量。然而，目前的方法主要集中于单个项目，缺乏对建筑公司多个项目的综合规划和优化。本文描述了使用深度强化学习 (DRL) 代理和双深度 Q 网络等方法对自主资源分配 (ARA) 模型进行的模拟，并结合经验回放来开发和测试基于物联网数据收集的 ARA 算法 (物联网）设备。结果表明，DRL 可以通过捕获资源分配特征之间的复杂交互来成功执行 ARA，而无需在情况发生变化时进行重新训练。它为建筑公司提高更大、更复杂的建筑项目的资源利用率和项目绩效提供了广阔的前景。