This work presents a distributed algorithm for resolving cooperative multi-vehicle conflicts in highly constrained spaces. By formulating the conflict resolution problem as a Multi-Agent Reinforcement Learning (RL) problem, we can train a policy offline to drive the vehicles towards their destinations safely and efficiently in a simplified discrete environment. During the online execution, each vehicle first simulates the interaction among vehicles with the trained policy to obtain its strategy, which is used to guide the computation of a reference trajectory. A distributed Model Predictive Controller (MPC) is then proposed to track the reference while avoiding collisions. The preliminary results show that the combination of RL and distributed MPC has the potential to guide vehicles to resolve conflicts safely and smoothly while being less computationally demanding than the centralized approach.

翻译：本文提出了一种分布式算法，用于解决高度受限空间中多车辆协作冲突消解问题。通过将冲突消解问题建模为多智能体强化学习问题，我们可以在简化离散环境中离线训练策略，引导车辆安全高效地驶向目的地。在线执行阶段，每辆车首先利用训练好的策略模拟车辆间的交互以获取其策略，进而用于指导参考轨迹的生成。随后，提出一种分布式模型预测控制器实现参考轨迹跟踪并避免碰撞。初步结果表明，强化学习与分布式模型预测控制的结合具有引导车辆安全平稳解决冲突的潜力，且相较于集中式方法计算负荷更低。