A Robust Utility Learning Framework via Inverse Optimization
نویسندگان
چکیده
منابع مشابه
A Robust Utility Learning Framework via Inverse Optimization
In many smart infrastructure applications, flexibility in achieving sustainability goals can be gained by engaging end users. However, these users often have heterogeneous preferences that are unknown to the decision maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between noncooperative players, we propose a robust parametric utility learning f...
متن کاملSmart Building Energy Efficiency via Social Game: A Robust Utility Learning Framework for Closing–the–Loop
Given a non-cooperative, continuous game, we describe a framework for parametric utility learning. Using heteroskedasticity inference, we adapt a Constrained Feasible Generalized Least Squares (cFGLS) utility learning method in which estimator variance is reduced, unbiased, and consistent. We extend our utility learning method using bootstrapping and bagging. We show the performance of the prop...
متن کاملOracle-Based Robust Optimization via Online Learning
Robust optimization is a common framework in optimization under uncertainty when the problem parameters are not known, but it is rather known that the parameters belong to some given uncertainty set. In the robust optimization framework the problem solved is a min-max problem where a solution is judged according to its performance on the worst possible realization of the parameters. In many cas...
متن کاملGuided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Reinforcement learning can acquire tcomplex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addre...
متن کاملA general framework for evolutionary multiobjective optimization via manifold learning
Under certain mild condition, the Pareto-optimal set (PS) of a continuous multiobjective optimization problem, with m objectives, is a piece-wise continuous (m 1)-dimensional manifold. This regularity property is important, yet has been unfortunately ignored in many evolutionary multiobjective optimization (EMO) studies. The first work that explicitly takes advantages of this regularity propert...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Control Systems Technology
سال: 2018
ISSN: 1063-6536,1558-0865
DOI: 10.1109/tcst.2017.2699163