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：这项工作提出了解决高度受限空间多车辆合作冲突的分布式算法;通过将解决冲突问题作为多机构强化学习(RL)问题加以阐述,我们可以在离散环境中对政策进行离线培训,以便在简化的环境中安全、高效地将车辆驶向目的地;在网上执行过程中,每部车辆首先模拟车辆与经过培训的政策之间的互动,以获得其战略,用于指导参考轨迹的计算;然后提出分发的模型预测主计长(MPC),以跟踪参考文献,同时避免碰撞;初步结果显示,将机动车和分布式机动车结合起来,有可能引导车辆安全、顺利地解决冲突,同时在计算上比集中办法要求更少。