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Found 10 papers, 0 papers with code
Tsallis Entropy Regularization for Linearly Solvable MDP and Linear Quadratic Regulator
Shannon entropy regularization is widely adopted in optimal control due to its ability to promote exploration and enhance robustness, e. g., maximum entropy reinforcement learning known as Soft Actor-Critic.
Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning
Considering this, there is a pressing need for research to enhance the robustness of network systems.
Sampled-Data Primal-Dual Gradient Dynamics in Model Predictive Control
In this paper, we propose a discrete-time dynamical controller, incorporating specific modifications to the PDG approach, and present stability conditions relevant to the resulting sampled-data system.
Learning Exactly Linearizable Deep Dynamics Models
Research on control using models based on machine-learning methods has now shifted to the practical engineering stage.
An LMI Framework for Contraction-based Nonlinear Control Design by Derivatives of Gaussian Process Regression
Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian matrices.
Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost
In this study, we proposed a method for designing a resilient logistics network based on entropy regularization.
Maximum entropy optimal density control of discrete-time linear systems and Schrödinger bridges
We consider an entropy-regularized version of optimal density control of deterministic discrete-time linear systems.
Kullback-Leibler control for discrete-time nonlinear systems on continuous spaces
To avoid such approximation, in this paper, we reformulate the KL control problem for continuous spaces so that it does not require unrealistic assumptions.
Learning Stabilizable Deep Dynamics Models
When neural networks are used to model dynamics, properties such as stability of the dynamics are generally not guaranteed.
Structured Hammerstein-Wiener Model Learning for Model Predictive Control
This paper aims to improve the reliability of optimal control using models constructed by machine learning methods.
