Bike-sharing systems play a crucial role in easing traffic congestion and
promoting healthier lifestyles. However, ensuring their reliability and user
acceptance requires effective strategies for rebalancing bikes. This study
introduces a novel approach to address the real-time rebalancing problem with a
fleet of vehicles. It employs a dual policy reinforcement learning algorithm
that decouples inventory and routing decisions, enhancing realism and
efficiency compared to previous methods where both decisions were made
simultaneously. We first formulate the inventory and routing subproblems as a
multi-agent Markov Decision Process within a continuous time framework.
Subsequently, we propose a DQN-based dual policy framework to jointly estimate
the value functions, minimizing the lost demand. To facilitate learning, a
comprehensive simulator is applied to operate under a first-arrive-first-serve
rule, which enables the computation of immediate rewards across diverse demand
scenarios. We conduct extensive experiments on various datasets generated from
historical real-world data, affected by both temporal and weather factors. Our
proposed algorithm demonstrates significant performance improvements over
previous baseline methods. It offers valuable practical insights for operators
and further explores the incorporation of reinforcement learning into
real-world dynamic programming problems, paving the way for more intelligent
and robust urban mobility solutions.

自行车共享系统扮演了缓解交通拥堵和促进健康生活方式的关键角色。然而，保证其可靠性和用户接受度需要有效的自行车再平衡策略。本研究引入了一种新颖的方法来解决实时再平衡问题，采用车辆车队的双策略强化学习算法，通过分开库存和路径决策来提高现实性和效率。我们首先将库存和路径子问题在连续时间框架内构建成多智能体马尔可夫决策过程，并随后提出了基于 DQN 的双策略框架来联合估计值函数，从而减少流失的需求。通过应用综合性模拟器在先到先服务规则下运行，该模拟器可以计算出不同需求场景下的即时奖励，以便于学习。我们对由历史真实数据生成的各种数据集进行了大量实验，这些数据集受到时间和天气因素的影响。我们的算法在性能上显著优于以往的基准方法。它为运营商提供了有价值的实践观察，并进一步探讨了将强化学习应用于现实动态规划问题中的可能性，为更智能、更健壮的城市移动解决方案铺平了道路。