通过深度强化学习的自动驾驶车辆巡逻：学习通信与合作
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate
AI总结：
本文提出基于强化智能体间学习（RIAL）的多智能体强化学习方案，解决自动驾驶车辆在复杂环境下的巡逻通信与合作问题，并在仿真中验证其优于现有方法的性能与容错能力。
摘要：
自动驾驶车辆适用于连续区域巡逻问题。由于未知的环境因素（如风或地形）或自动驾驶车辆的约束（如电池寿命有限或硬件故障），寻找最优巡逻策略具有挑战性。重要的是，巡逻大面积区域通常需要多个智能体共同协调其行动。然而，由于巡逻环境的复杂性质，手动定义最优协调策略通常并非易事。在本文中，我们考虑了一个包含环境因素、智能体限制和三个典型合作问题的巡逻问题——碰撞避免、拥堵避免和巡逻目标协商。我们提出了一种基于强化智能体间学习（RIAL）方法的多智能体强化学习解决方案。通过这种方法，智能体被训练以开发自己的通信协议，从而在可能发生且确实发生故障的巡逻过程中进行合作。该解决方案通过仿真实验进行了验证，并从不同视角与几种最先进的巡逻解决方案进行了比较，包括整体巡逻性能、碰撞避免性能、电池充电策略的效率以及整体容错能力。
英文摘要：
Autonomous vehicles are suited for continuous area patrolling problems. Finding an optimal patrolling strategy can be challenging due to unknown environmental factors, such as wind or landscape; or autonomous vehicles' constraints, such as limited battery life or hardware failures. Importantly, patrolling large areas often requires multiple agents to collectively coordinate their actions. However, an optimal coordination strategy is often non-trivial to be manually defined due to the complex nature of patrolling environments. In this paper, we consider a patrolling problem with environmental factors, agent limitations, and three typical cooperation problems -- collision avoidance, congestion avoidance, and patrolling target negotiation. We propose a multi-agent reinforcement learning solution based on a reinforced inter-agent learning (RIAL) method. With this approach, agents are trained to develop their own communication protocol to cooperate during patrolling where faults can and do occur. The solution is validated through simulation experiments and is compared with several state-of-the-art patrolling solutions from different perspectives, including the overall patrol performance, the collision avoidance performance, the efficiency of battery recharging strategies, and the overall fault tolerance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
