Multi-Vehicle Cooperative Decision-Making in Merging Area Based on Deep Multi-Agent Reinforcement Learning

In recent years, reinforcement learning (RL) methods have shown powerful learning capabilities in single-vehicle autonomous driving. However, few studies have focused on multi-vehicle cooperative driving based on RL, particularly in the dynamically changing traffic environments of highway ramp merge zones. In this paper, a multi-agent deep reinforcement learning (MARL) framework for multi-vehicle cooperative decision-making is proposed based on actor–critic, which categorizes vehicles into two groups according to their origins in the merging area. At the same time, the complexity of the network is reduced and the training process of the model is accelerated by utilizing mechanisms such as partial parameter sharing and experience playback. Additionally, a combination of global and individual rewards is adopted to promote cooperation in connected autonomous vehicles (CAVs) and balance individual and group interests. The training performance of the model is compared under three traffic densities, and our method is also compared with state-of-the-art benchmark methods. The simulation results show that the proposed MARL framework can have stronger policy learning capability and stability under various traffic flow conditions. Moreover, it can also effectively improve the speed of vehicles in the merging zone and reduce traffic conflicts.