Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm's reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm's efficacy in handling different objectives, highlighting its potential to enhance safety and efficiency in autonomous driving within lane-free traffic environments.

翻译：深度强化学习在包括车辆交通控制在内的多种工程应用中展现出潜力。交通的非平稳特性，尤其是在车辆行为自由度更高的无车道环境中，为决策制定带来了挑战，因为错误的行动可能导致灾难性后果。本文提出一种基于竞争性多智能体深度确定性策略梯度方法的网联自动驾驶车辆新型驾驶策略。所开发的多智能体深度强化学习算法创建了动态非平稳场景，模拟真实世界交通的复杂性，使训练后的智能体更具鲁棒性。该算法的奖励函数经战略性独特设计，涵盖多个车辆控制任务，包括保持期望速度、超车、防碰撞以及汇入与分流操作。此外，还综合考虑了横向与纵向乘客舒适度及安全标准。我们采用被称为推斥力与排斥力的车辆间作用力来管理无车道交通环境中网联自动驾驶车辆的运行。所提出的驾驶算法在无车道道路上通过城市交通仿真平台进行训练与评估。实验结果表明该算法能有效处理不同目标，突显了其在无车道交通环境中提升自动驾驶安全性与效率的潜力。