基于深度强化学习的异构车队路径选择
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles
摘要：
基于深度强化学习（DRL）在协作多智能体系统中的应用前景，提出了一种求解具有固定车队规模的多车辆路径问题（CMVRP）的模型和学习过程。我们的学习过程遵循集中训练和分散执行的范式。我们实证测试我们的模型，并显示其能力，通过合作行动产生接近最优的解决方案。在大的情况下，我们的模型产生更好的解决方案比其他常用的算法。此外，我们的模型可以解决CMVRP的任意实例，而无需重新训练。
英文摘要：
Motivated by the promising advances of deep-reinforcement learning (DRL) applied to cooperative multi-agent systems we propose a model and learning procedure to solve the Capacitated Multi-Vehicle Routing Problem (CMVRP) with fixed fleet size. Our learning procedure follows a centralized-training and decentralized-execution paradigm. We empirically test our model and showed its capability for producing near-optimal solutions through cooperative actions. In large instances, our model generates better solutions than other commonly used heuristics. Additionally, our model can solve arbitrary instances of the CMVRP without requiring re-training.
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