用GPU加速可微分模型预测控制，提升强化学习训练速度。
Differentiable Model Predictive Control on the GPU
- 基于序列二次规划与三对角预处理共轭梯度法，实现GPU并行优化。
- 在基准任务上训练速度显著快于现有CPU/GPU方法。
- 适用于高速驾驶等复杂控制场景，支持极限工况下的稳定漂移。
可微分模型预测控制（MPC）为融合学习与控制提供了强大框架，但其应用受限于传统优化算法固有的串行特性，难以在现代硬件如GPU上并行化。本文提出一种GPU加速的可微分优化求解器，采用序列二次规划与自定义三对角预处理共轭梯度（PCG）算法，有效利用问题结构实现高效并行。实验表明，该方法相比基于CPU和GPU的基线显著提升速度，在基准强化学习与模仿学习任务中大幅缩短训练时间。最后，我们在高难度驾驶任务中验证了该方法，成功实现丰田普锐斯在湿滑路面的稳健漂移控制。
原文摘要 · Abstract (English)
Differentiable model predictive control (MPC) offers a powerful framework for combining learning and control. However, its adoption has been limited by the inherently sequential nature of traditional optimization algorithms, which are challenging to parallelize on modern computing hardware like GPUs. In this work, we tackle this bottleneck by introducing a GPU-accelerated differentiable optimization tool for MPC. This solver leverages sequential quadratic programming and a custom preconditioned conjugate gradient (PCG) routine with tridiagonal preconditioning to exploit the problem's structure and enable efficient parallelization. We demonstrate substantial speedups over CPU- and GPU-based baselines, significantly improving upon state-of-the-art training times on benchmark reinforcement learning and imitation learning tasks. Finally, we showcase the method on the challenging task of reinforcement learning for driving at the limits of handling, where it enables robust drifting of a Toyota Supra through water puddles.
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