Variational Inference MPC using Tsallis Divergence

• URL: http://arxiv.org/abs/2104.00241v1
• Date: Thu, 1 Apr 2021 04:00:49 GMT
• Title: Variational Inference MPC using Tsallis Divergence
• Authors: Ziyi Wang, Oswin So, Jason Gibson, Bogdan Vlahov, Manan S. Gandhi, Guan-Horng Liu and Evangelos A. Theodorou
• Abstract summary: We provide a framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. A novel Tsallis Variational Inference-Model Predictive Control algorithm is derived.
• Score: 10.013572514839082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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