25 citations · 38 across the 14 of their papers we have counts for
6 papers · 1 filter
A Control-Barrier-Function-Based Algorithm for Policy Adaptation in Reinforcement Learning
Wenjian Hao, Zehui Lu, Nicolas Miguel +1
This paper considers the problem of adapting a predesigned policy, represented by a parameterized function class, from a solution that minimizes a given original cost function to a…
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
Distributed Koopman Learning with Incomplete Measurements
Wenjian Hao, Lili Wang, Ayush Rai +1
Koopman operator theory has emerged as a powerful tool for system identification, particularly for approximating nonlinear time-invariant systems (NTIS). This paper considers a net…
Deep Koopman Learning using Noisy Data
Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under nois…
A Data-Driven Approach for Inverse Optimal Control
Zihao Liang, Wenjian Hao, Shaoshuai Mou
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its…
Deep Koopman Learning of Nonlinear Time-Varying Systems
Wenjian Hao, Bowen Huang, Wei Pan +2
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the…