A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
Robust Reinforcement Learning: A Review of Foundations and Recent Advances
2022
Machine Learning and Knowledge Extraction
Preserved Fulltext
Reinforcement learning (RL) has become a highly successful framework for learning in Markov decision processes (MDP). ...
We survey the literature on robust approaches to reinforcement learning and categorize these methods in four different ways: (i) Transition robust designs account for uncertainties in the system dynamics ...
Acknowledgments: We thank Joe Watson from the Intelligent Autonomous System group at TU Darmstadt for his constructive feedback and support. ...
doi:10.3390/make4010013
fatcat:ifa3z7cx7rc7homa4flywxvhvi
DATA-DRIVEN OPTIMIZATION UNDER UNCERTAINTY IN THE ERA OF BIG DATA AND DEEP LEARNING: GENERAL FRAMEWORKS, ALGORITHMS, AND APPLICATIONS
2020
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In the first related project, we propose a novel data-driven Wasserstein distributionally robust mixedinteger nonlinear programming model for the optimal biomass with agricultural wasteto-energy network ...
The finite convergence of the proposed solution algorithm is guaranteed for the multistage robust optimization problem with a generic uncertainty set. ...
for uncertain systems. ...
doi:10.7298/ywzn-4d48
fatcat:is6nc2hhb5gcxnwfm6r4chepgy
On decomposability in robot reinforcement learning
[article]
2017
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The first case study deals with the problem of ball catching: how to run most effectively to catch a projectile, such as a baseball, that is flying in the air for a long period of time. ...
Reinforcement learning is a computational framework that enables machines to learn from trial-and-error interaction with the environment. ...
A.3.2 Iterative LQR (iLQR) LQR is only applicable to problems that exhibit linear system dynamics and a quadratic cost function. ...
doi:10.14279/depositonce-6054
fatcat:w3k6rvlg3veq5fcabygjhewabi