CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control
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...
| Authors | Antoonio Buo, Vittorio Cammarota, Michele Avagnale et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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