Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.

中文翻译：

---

使用多智能体强化学习的自主巴士车队控制

自动驾驶公交车正变得越来越流行，并在许多国家得到了广泛的发展。然而，自动驾驶巴士必须学会有效地在城市中行驶，才能融入公共交通系统。智能代理可以通过强化学习来实现这些总线的高效运行。在这项研究中，我们研究了自动巴士车队控制问题，由于随机到达和对环境的不完整观察，这对代理显得嘈杂。针对这个大规模动态优化问题，我们提出了一种结合高级策略梯度算法的多智能体强化学习方法。开发了一个基于代理的仿真平台，用于对固定停靠站/车站环路、自主公交车队和乘客的动态系统进行建模。该平台还用于评估所提出算法的性能。实验结果表明，所开发的算法在多智能体领域优于其他强化学习方法。模拟结果还揭示了我们提出的算法在公交车队规模和乘客人数相对较少的公交路线的乘客等待时间方面优于现有的定期公交系统的有效性。