提出快速求解约束多智能体博弈的牛顿法新算法。

Residual Descent Differential Dynamic Game (RD3G) -- A Fast Newton Solver for Constrained General Sum Games

• 基于牛顿法，动态维护激活约束集并结合障碍函数。
• 在多个测试问题上计算效率显著优于现有方法。
• 适合需要快速求解约束博弈的机器人协同控制场景。

我们提出残差下降微分动态博弈（RD3G），一种用于约束多智能体博弈控制问题的牛顿型求解器。该方法针对代理间通过奖励和状态约束耦合的问题，寻找局部纳什均衡。通过与当前最先进的技术对比，展示了在多个示例问题中RD3G算法的计算优势。该方法通过维持动态激活约束集、对满足约束使用障碍函数，并结合回溯线搜索策略，实现高效收敛。

原文摘要 · Abstract (English)

We present Residual Descent Differential Dynamic Game (RD3G), a Newton-based solver for constrained multi-agent game-control problems. The proposed solver seeks a local Nash equilibrium for problems where agents are coupled through their rewards and state constraints. We compare the proposed method against competing state-of-the-art techniques and showcase the computational benefits of the RD3G algorithm on several example problems.

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