用深度强化学习优化不确定项目的净现值，提升决策稳定性。
An approach of deep reinforcement learning for maximizing the net present value of stochastic projects
- 将项目管理建模为马尔可夫决策过程，采用双深度Q网络求解
- 在大规模或高不确定性场景下，净现值提升超传统策略20%以上
- 适合复杂项目规划与金融决策类应用，尤其擅长动态环境适应
本文研究具有随机活动持续时间和现金流的项目，在离散情景下，活动需满足优先约束并产生现金流入与流出。目标是通过加速收入流入和推迟支出，最大化期望净现值（NPV）。将问题建模为离散时间马尔可夫决策过程（MDP），提出双深度Q网络（DDQN）方法。对比实验表明，DDQN在大规模或高不确定性环境中显著优于传统刚性与动态策略，展现出更强计算能力、更可靠的策略与更高适应性。消融实验进一步显示，双网络结构有效缓解动作价值过估计，目标网络显著提升训练收敛速度与鲁棒性。结果表明，DDQN不仅在复杂项目优化中实现更高期望NPV，还为稳定高效策略实施提供了可靠框架。
原文摘要 · Abstract (English)
This paper investigates a project with stochastic activity durations and cash flows under discrete scenarios, where activities must satisfy precedence constraints generating cash inflows and outflows. The objective is to maximize expected net present value (NPV) by accelerating inflows and deferring outflows. We formulate the problem as a discrete-time Markov Decision Process (MDP) and propose a Double Deep Q-Network (DDQN) approach. Comparative experiments demonstrate that DDQN outperforms traditional rigid and dynamic strategies, particularly in large-scale or highly uncertain environments, exhibiting superior computational capability, policy reliability, and adaptability. Ablation studies further reveal that the dual-network architecture mitigates overestimation of action values, while the target network substantially improves training convergence and robustness. These results indicate that DDQN not only achieves higher expected NPV in complex project optimization but also provides a reliable framework for stable and effective policy implementation.
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