mlsanityData to 7 Oct 2026

Variational Inference MPC using Tsallis Divergence

arXiv 2104.00241 ↗First posted 1 Apr 2021cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimality likelihood function, a novel Tsallis Variational Inference-Model Predictive Control algorithm is derived, which includes prior works such as Variational Inference-Model Predictive Control, Model Predictive PathIntegral Control, Cross Entropy Method, and Stein VariationalInference Model Predictive Control as special cases. The proposed algorithm allows for effective control of the cost/reward transform and is characterized by superior performance in terms of mean and variance reduction of the associated cost. The aforementioned features are supported by a theoretical and numerical analysis on the level of risk sensitivity of the proposed algorithm as well as simulation experiments on 5 different robotic systems with 3 different policy parameterizations.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

19 of 36 references.

References · 19

  1. 2016Variational Inference: A Review for Statisticianscited by 1,466
  2. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  3. 2017Advances in Variational Inferencecited by 236
  4. 2016R'enyi Divergence Variational Inferencecited by 104
  5. 2017Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Drivingcited by 81
  6. 2020Learning Robot Skills with Temporal Variational Inferencecited by 54
  7. 2017Message Passing Stein Variational Gradient Descentcited by 47
  8. 2019Variational Inference MPC for Bayesian Model-based Reinforcement Learningcited by 39
  9. 2020Stein Variational Model Predictive Controlcited by 29
  10. 2020f-Divergence Variational Inferencecited by 28
  11. 2018Variational Inference with Tail-adaptive f-Divergencecited by 25
  12. 2019FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulationcited by 20
7 more
  1. 2019Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learningcited by 18
  2. 2018Alpha-Beta Divergence For Variational Inferencecited by 12
  3. 2019Non-Smooth Newton Methods for Deformable Multi-Body Dynamicscited by 7
  4. 2013Gradient-Based Adaptive Stochastic Search for Non-Differentiable Optimizationcited by 4
  5. 2019Constrained Sampling-based Trajectory Optimization using Stochastic Approximationcited by 3
  6. 2018Information Theoretic Model Predictive Control on Jump Diffusion Processescited by 1
  7. 2020Adaptive Risk Sensitive Model Predictive Control with Stochastic Searchcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 3; 2025: 3.

11 papers.

  1. 2022Your Policy Regularizer is Secretly an Adversarycited by 21
  2. 2022Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projectioncited by 13
  3. 2022Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processescited by 10
  4. 2023Constrained Stein Variational Trajectory Optimizationcited by 8
  5. 2024CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Designcited by 3
  6. 2024Information-Theoretic Measures on Lattices for High-Order Interactionscited by 3
  7. 2024Risk-sensitive control as inference with R'enyi divergencecited by 3
  8. 2025Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Taskscited by 3
  9. 2023NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Predictioncited by 0
  10. 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
  11. 2025Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identificationcited by 0