深度且快速思考：从逆向动态博弈中学习神经网络非线性观点动力学以实现瞬间交互
Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions
AI总结：
针对自主智能体在复杂多智能体交互中无法快速决策导致死锁的问题，首次提出基于学习与博弈论的Neural NOD模型，从专家演示中自适应学习参数，在模拟赛车中实现了快速、无死锁且优于基线的决策。
摘要：
非合作交互常见于赛车等多智能体场景，其中自车可选择超车对手，或保持跟车直到安全的超车“通道”出现。尽管专家人类能做出此类时间敏感决策，自主智能体却无法快速推理复杂且可能冲突的选项，导致死锁等次优行为。近期，非线性观点动力学（NOD）模型被证明能实现快速观点形成并避免决策死锁。然而，NOD建模参数常被假设为固定值，限制了其在复杂动态环境中的适用性。如何自动且自适应地确定此类参数以应对不断变化的环境，仍是一个开放挑战。在本工作中，我们首次提出一种基于学习与博弈论的方法，从专家演示（即包含交互智能体（可能不完整）状态和动作轨迹的数据集）中合成神经NOD模型。我们在模拟自动驾驶赛车示例中展示了Neural NOD做出快速且无死锁决策的能力。我们发现，Neural NOD在安全性和超车性能方面始终优于最先进的数据驱动逆向博弈基线。
英文摘要：
Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking "corridor" opens. While an expert human can do well at making such time-sensitive decisions, autonomous agents are incapable of rapidly reasoning about complex, potentially conflicting options, leading to suboptimal behaviors such as deadlocks. Recently, the nonlinear opinion dynamics (NOD) model has proven to exhibit fast opinion formation and avoidance of decision deadlocks. However, NOD modeling parameters are oftentimes assumed fixed, limiting their applicability in complex and dynamic environments. It remains an open challenge to determine such parameters automatically and adaptively, accounting for the ever-changing environment. In this work, we propose for the first time a learning-based and game-theoretic approach to synthesize a Neural NOD model from expert demonstrations, given as a dataset containing (possibly incomplete) state and action trajectories of interacting agents. We demonstrate Neural NOD's ability to make fast and deadlock-free decisions in a simulated autonomous racing example. We find that Neural NOD consistently outperforms the state-of-the-art data-driven inverse game baseline in terms of safety and overtaking performance.
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。
