Variational Inference MPC using Tsallis Divergence
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2021
- DOI:
- 10.48550/arXiv.2104.00241
- arXiv:
- arXiv:2104.00241
- Bibcode:
- 2021arXiv210400241W
- Keywords:
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- Computer Science - Machine Learning