用CUDA加速强化学习的模型预测控制，大幅降低训练推理时间。
CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control
- 用CUDA加速可微分MPC层，提升计算效率
- 无人机竞速任务中实现顶尖圈速与低延迟
- 适合需要实时控制的复杂系统应用
在文献中，基于强化学习的模型预测控制（AC-MPC）通过融合MPC与强化学习，实现了对复杂动态系统的高性能控制。然而，其可微分MPC层在前向和反向传播中需反复求解优化问题，导致训练与推理延迟显著。本文提出一种CUDA加速的AC-MPC变体，在保持基准性能的前提下，显著缩短端到端执行时间。在敏捷无人机竞速任务上的仿真结果表明，该方法实现了顶尖圈速，接近极限动态行为，同时训练与推理时间明显减少。
原文摘要 · Abstract (English)
In the literature, actor-critic model predictive control (AC-MPC) integrates MPC with reinforcement learning to enable high-performance control of complex dynamical systems. However, its differentiable MPC layer requires repeatedly solving an optimization problem in both the forward and backward passes, leading to substantial training and inference latency. This paper tackles this bottleneck introducing a CUDA-accelerated variant that significantly reduces end-to-end execution time while preserving the control performance of the baseline formulation. Simulation results on an agile drone racing task show that our approach achieves state-of-the-art lap times and near-limit dynamic behaviour with markedly reduced training and inference time.
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