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.

翻译：在本文中,我们通过使用非广度 Tsallis 差异,为变化推断-随机最佳控制提供了一个通用框架。通过将变形指数函数纳入最佳可能性功能,我们得出了一个新的 Tsallis variational Inference-Model 预测控制算法,其中包括以前的工作,如变异推断-模型预测控制、模型预测路径控制、跨肠法以及作为特殊情况的 Stein Variational Inference 模型预测控制。提议的算法允许有效控制成本/调值变换,其特点是相关成本的平均值和差异减少方面表现优异。上述特征得到关于拟议算法风险敏感性程度的理论和数字分析的支持,以及对5个不同机器人系统进行模拟实验,并有3个不同的政策参数。