CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control

• URL: http://arxiv.org/abs/2605.29155v1
• Date: Wed, 27 May 2026 22:38:09 GMT
• Title: CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control
• Abstract summary: Actor-critic model predictive control (AC-MPC) integrates MPC with reinforcement learning to enable high-performance control of dynamical systems.<n>This paper tackles this bottleneck introducing a performance-accelerated variant that significantly reduces end-to-end execution time.<n> Simulation results on an agile drone racing task show that our approach markedly achieves state-of-the-art lap times and near-limit behaviour with reduced training and inference time.
• Score: 1.893395217315348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Real-Time Control-Constrained DDP for Underactuated Balancing of Legged Robots [0.28106259549258145]
  This paper presents a real-time control-constrained Differential Dynamic Programming (DDP) framework for underactuated legged robots.<n>To the best of our knowledge, this is the first demonstration of static two-leg standing of a quadruped robot achieved using real-time finite-horizon MPC.
  arXiv  Detail & Related papers  (2026-08-19T05:21:27Z)
• Learning-enabled Acceleration of Scenario-based Model Predictive Control [1.838884769821944]
  This paper presents a learning-accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for efficiently solving scenario-based model predictive control (SBMPC) problems.<n>We reformulate the SBMPC problems into consensus forms that can be decomposed via ADMM, separating the scenario-dependent dynamics from non-anticipativity constraints.<n>Building on this decomposition, we utilize existing learning-to-optimize schemes, which leverages Moreau envelope learning of the cost function to accelerate the primal update in ADMM.
  arXiv  Detail & Related papers  (2026-07-14T13:48:48Z)
• AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference [0.0]
  Fixed-depth speculative decoding has emerged as one promising technique, but in practice, it often leads to performance degradation due to bandwidth saturation, instability, or even catastrophic resource exhaustion resulting in system failure.<n>Our solution consists of four tightly-coupled components working together in a continuous feedback loop: an Adaptive Draft Controller, a Dynamic Policy Engine, a KV Cache Coordination Layer, and a Monitoring Engine.<n>While conventional approaches focus solely on maximizing throughput, we instead assess the effectiveness of our approach based on several key metrics including wasted drafted compute and inter-token latency dispersion alongside standard measures of speculative efficiency.
  arXiv  Detail & Related papers  (2026-07-04T13:39:40Z)
• Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach [1.6567880228735359]
  Model Predictive Path Integral (MPPI) control is a powerful sampling-based strategy for nonlinear autonomous systems.<n>We propose ICODE-MPPI, a robust framework that leverages Input Concomitant Ordinary Differential Equations (ICODEs) to learn and compensate for unmodeled residual dynamics.<n>High-fidelity simulations on complex trajectories demonstrate that ICODE-MPPI achieves up to a 69% reduction in cross-tracking error under persistent disturbances.
  arXiv  Detail & Related papers  (2026-05-05T01:20:51Z)
• DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models [61.26694413585136]
  Drift-Aware Post-Training Quantization (DA-PTQ) formulates quantization as a drift-aware optimization problem over sequential decision processes.<n>DA-PTQ significantly reduces kinematic drift and achieves comparable performance to full-precision models under low-bit settings.
  arXiv  Detail & Related papers  (2026-04-13T14:51:43Z)
• Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion [15.125805248577278]
  We propose a cost-matching approach for optimal humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning framework.<n>A parameterized MPC formulation with centroidal dynamics is trained to approximate the action-value function obtained from high-fidelity closed-loop data.<n>Results demonstrate improved locomotion performance and robustness to model mismatch and external disturbances.
  arXiv  Detail & Related papers  (2026-03-30T10:05:15Z)
• Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization [81.35990332700389]
  Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network.<n>During deployment, the parameters of the underlying MPC must usually be fine-tuned.<n>Recent work addresses this problem by adapting AMPC without retraining using approximated sensitivities of the MPC's optimization problem.
  arXiv  Detail & Related papers  (2025-12-16T12:24:08Z)
• TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration [64.32072516882947]
  Diffusion Policy excels in embodied control but suffers from high inference latency and computational cost.<n>We propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP)<n>TS-DP achieves up to 4.17 times faster inference with over 94% accepted drafts, reaching an inference frequency of 25 Hz.
  arXiv  Detail & Related papers  (2025-12-13T07:53:14Z)
• Bisimulation metric for Model Predictive Control [44.301098448479195]
  Bisimulation Metric for Model Predictive Control (BS-MPC) is a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite.
  arXiv  Detail & Related papers  (2024-10-06T17:12:10Z)
• When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL [37.58940726230092]
  Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP) We formalize an RL framework, Time-adaptive Control & Sensing (TaCoS), that tackles this challenge. We demonstrate that state-of-the-art RL algorithms trained on TaCoS drastically reduce the interaction amount over their discrete-time counterpart.
  arXiv  Detail & Related papers  (2024-06-03T09:57:18Z)
• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• Layer Pruning on Demand with Intermediate CTC [50.509073206630994]
  We present a training and pruning method for ASR based on the connectionist temporal classification (CTC) We show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU.
  arXiv  Detail & Related papers  (2021-06-17T02:40:18Z)
• Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven [80.94390916562179]
  Time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. It is desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. We show how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP.
  arXiv  Detail & Related papers  (2020-06-16T05:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.