mlsanityData to 13 Oct 2026

CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control

arXiv 2408.09662 ↗First posted 19 Aug 2024cs.RO cs.DCcited by 4

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi, an extension of the CasADi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. First posted on arXiv 19 Aug 2024.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2026Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPUcited by 7
  2. 2025Differentiable Model Predictive Control on the GPUcited by 5
  3. 2025Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networkscited by 3
  4. 2026TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPUcited by 2