mlsanityData to 7 Oct 2026

Model Predictive Path Integral Control using Covariance Variable Importance Sampling

arXiv 1509.01149 ↗First posted 3 Sep 2015eess.SY cs.DC cs.ROcited by 122

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Cited by 33 in World models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper we develop a Model Predictive Path Integral (MPPI) control algorithm based on a generalized importance sampling scheme and perform parallel optimization via sampling using a Graphics Processing Unit (GPU). The proposed generalized importance sampling scheme allows for changes in the drift and diffusion terms of stochastic diffusion processes and plays a significant role in the performance of the model predictive control algorithm. We compare the proposed algorithm in simulation with a model predictive control version of differential dynamic programming.

Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2015.

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Citing papers per year: 2021: 8; 2022: 13; 2023: 25; 2024: 21; 2025: 26; 2026: 29.

122 papers.

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  11. 2022A General Framework for Sample-Efficient Function Approximation in Reinforcement Learningcited by 31
  12. 2024Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learningcited by 31
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  26. 2026VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Modelscited by 14
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  31. 2023Efficient Dynamics Modeling in Interactive Environments with Koopman Theorycited by 10
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  37. 2022EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Modelcited by 8
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