  Lane-changing decisions, which are crucial for autonomous vehicle path
planning, face practical challenges due to rule-based constraints and limited
data. Deep reinforcement learning has become a major research focus due to its
advantages in data acquisition and interpretability. However, current models
often overlook collaboration, which affects not only impacts overall traffic
efficiency but also hinders the vehicle's own normal driving in the long run.
To address the aforementioned issue, this paper proposes a method named Mix
Q-learning for Lane Changing(MQLC) that integrates a hybrid value Q network,
taking into account both collective and individual benefits for the greater
good. At the collective level, our method coordinates the individual Q and
global Q networks by utilizing global information. This enables agents to
effectively balance their individual interests with the collective benefit. At
the individual level, we integrated a deep learning-based intent recognition
module into our observation and enhanced the decision network. These changes
provide agents with richer decision information and more accurate feature
extraction for improved lane-changing decisions. This strategy enables the
multi-agent system to learn and formulate optimal decision-making strategies
effectively. Our MQLC model, through extensive experimental results,
impressively outperforms other state-of-the-art multi-agent decision-making
methods, achieving significantly safer and faster lane-changing decisions.
