Safe reinforcement learning using Wasserstein distributionally robust MPC and chance constraint
In this paper, we address the chance-constrained safe Reinforcement Learning (RL)
problem using the function approximators based on Stochastic Model Predictive Control …
problem using the function approximators based on Stochastic Model Predictive Control …
Data-driven distributionally robust MPC for constrained stochastic systems
P Coppens, P Patrinos - IEEE Control Systems Letters, 2021 - ieeexplore.ieee.org
In this letter we introduce a novel approach to distributionally robust optimal control that
supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We …
supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We …
Temporal robustness of stochastic signals
We study the temporal robustness of stochastic signals. This topic is of particular interest in
interleaving processes such as multi-agent systems where communication and individual …
interleaving processes such as multi-agent systems where communication and individual …
Emerging methodologies in stability and optimization problems of learning‐based nonlinear model predictive control: A survey
F Meng, X Shen, HR Karimi - International Journal of Circuit …, 2022 - Wiley Online Library
Since last 40 years, the theory and technology of model predictive control (MPC) have been
developed rapidly. However, nonlinear MPC still faces difficulties such as high online …
developed rapidly. However, nonlinear MPC still faces difficulties such as high online …
A general framework for learning-based distributionally robust MPC of Markov jump systems
M Schuurmans, P Patrinos - IEEE Transactions on Automatic …, 2023 - ieeexplore.ieee.org
In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …
chance-constrained Markov jump systems with unknown switching probabilities. Using …
Distributional robustness in minimax linear quadratic control with Wasserstein distance
To address the issue of inaccurate distributions in discrete-time stochastic systems, a
minimax linear quadratic control method using the Wasserstein metric is proposed. Our …
minimax linear quadratic control method using the Wasserstein metric is proposed. Our …
Interaction-aware model predictive control for autonomous driving
R Wang, M Schuurmans… - 2023 European Control …, 2023 - ieeexplore.ieee.org
We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …
merging tasks in automated driving. The MPC strategy is integrated with an online learning …
STL robustness risk over discrete-time stochastic processes
L Lindemann, N Matni… - 2021 60th IEEE …, 2021 - ieeexplore.ieee.org
We present a framework to interpret signal temporal logic (STL) formulas over discrete-time
stochastic processes in terms of the induced risk. Each realization of a stochastic process …
stochastic processes in terms of the induced risk. Each realization of a stochastic process …
Wasserstein distributionally robust control of partially observable linear systems: Tractable approximation and performance guarantee
A Hakobyan, I Yang - 2022 IEEE 61st Conference on Decision …, 2022 - ieeexplore.ieee.org
Wasserstein distributionally robust control (WDRC) is an effective method for addressing
inaccurate distribution information about disturbances in stochastic systems. It provides …
inaccurate distribution information about disturbances in stochastic systems. It provides …
Risk of stochastic systems for temporal logic specifications
The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …
intelligence and machine learning promise to enable many future technologies such as …