  Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL)
have shown impressive results in many driving scenarios. However, the
performance of these trained policies can be impacted when faced with diverse
driving styles and personalities, particularly in highly interactive
situations. This is because conventional MARL algorithms usually operate under
the assumption of fully cooperative behavior among all agents and focus on
maximizing team rewards during training. To address this issue, we introduce
the Personality Modeling Network (PeMN), which includes a cooperation value
function and personality parameters to model the varied interactions in
high-interactive scenarios. The PeMN also enables the training of a background
traffic flow with diverse behaviors, thereby improving the performance and
generalization of the ego vehicle. Our extensive experimental studies, which
incorporate different personality parameters in high-interactive driving
scenarios, demonstrate that the personality parameters effectively model
diverse driving styles and that policies trained with PeMN demonstrate better
generalization compared to traditional MARL methods.
