Efficient Learning of Differentiable MPC Policies on the GPU via Smoothed ADMM and First-Order Reinforcement Learning
Overview
This paper introduces SmoothedTurboMPC, a GPU-parallel differentiable model predictive control (MPC) solver that combines sequential quadratic programming with a log-barrier-smoothed alternating direction method of multipliers to address nonsmooth sensitivities and expensive evaluation. Paired with first-order reinforcement learning using Short-Horizon Actor-Critic, the approach reuses rollout MPC solutions to achieve up to 4.4x higher end-to-end training throughput. On a vehicle racing task, the method outperforms MPC PPO with a 46x speedup, training on 3.3 million transitions in 7 minutes. The framework is used to train a reliable MPC racing policy via RL with domain randomization and validated on a full-scale race car. The advances are integrated into the open-source TurboMPC solver.
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Reported quantity · Reported duration: 7 other · Basis not reported
“uses rollout MPC solutions instead of repeatedly re-evaluating them. Comparisons with state of the art solvers show that the approach achieves up to $4.4\times$ higher end-to-end training throughput. On a vehicle racing task, our method achieves higher returns than MPC PPO with a $46\times$ speedup, training on 3.3 million transitions in only 7 minutes. Finally, we use the framework to train a more reliable MPC racin”
Exact source · revision 1Source owner not reported
Reported quantity · speedup factor: 46 other · Basis not reported
“uses rollout MPC solutions instead of repeatedly re-evaluating them. Comparisons with state of the art solvers show that the approach achieves up to $4.4\times$ higher end-to-end training throughput. On a vehicle racing task, our method achieves higher returns than MPC PPO with a $46\times$ speedup, training on 3.3 million transitions in only 7 minutes. Finally, we use the framework to train a more reliable MPC racin”
Exact source · revision 1Source owner not reported
Reported quantity · throughput multiplier: 4.4 other · Basis not reported
“uses rollout MPC solutions instead of repeatedly re-evaluating them. Comparisons with state of the art solvers show that the approach achieves up to $4.4\times$ higher end-to-end training throughput. On a vehicle racing task, our method achieves higher returns than MPC PPO with a $46\times$ speedup, training on 3.3 million transitions in only 7 minutes. Finally, we use the framework to train a more reliable MPC racin”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalEfficient Learning of Differentiable MPC Policies on the GPU via Smoothed ADMM and First-Order Reinforcement Learning
This paper introduces SmoothedTurboMPC, a GPU-parallel differentiable model predictive control (MPC) solver that combines sequential quadratic programming with a log-barrier-smoothed alternating direction method of multipliers to address nonsmooth sensitivities and expensive evaluation. Paired with first-order reinforcement learning using Short-Horizon Actor-Critic, the approach reuses rollout MPC solutions to achieve up to 4.4x higher end-to-end training throughput. On a vehicle racing task, the method outperforms MPC PPO with a 46x speedup, training on 3.3 million transitions in 7 minutes. The framework is used to train a reliable MPC racing policy via RL with domain randomization and validated on a full-scale race car. The advances are integrated into the open-source TurboMPC solver.
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