在高度交互的自动驾驶场景中利用多智能体强化学习建模多样化驾驶行为
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
AI总结：
本文提出个性建模网络PeMN，通过合作价值函数和个性参数建模高交互场景中的多样化驾驶行为，并训练背景交通流以提升自车策略的泛化能力。
摘要：
通过多智能体强化学习（MARL）训练的自动驾驶车辆已在许多驾驶场景中展现出令人印象深刻的结果。然而，当面对多样化的驾驶风格和个性时，尤其是在高度交互的情境中，这些已训练策略的性能可能受到影响。这是因为传统的MARL算法通常在所有智能体完全合作的假设下运行，并在训练过程中专注于最大化团队奖励。为解决这一问题，我们提出了个性建模网络（PeMN），该网络包含一个合作价值函数和个性参数，用于建模高交互场景中的多样化交互。PeMN还支持训练具有多样化行为的背景交通流，从而提升自车的性能和泛化能力。我们广泛开展了实验研究，在高度交互的驾驶场景中引入不同的个性参数，结果表明个性参数能够有效建模多样化的驾驶风格，并且使用PeMN训练的策略相比传统MARL方法展现出更好的泛化性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
