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Robust Reinforcement Learning: A Review of Foundations and Recent Advances

Janosch Moos, Kay Hansel, Hany Abdulsamad, Svenja Stark, Debora Clever, Jan Peters
2022 Machine Learning and Knowledge Extraction  
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

Chao Ning
2020
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]

Sebastian Höfer, Technische Universität Berlin, Technische Universität Berlin, Oliver Brock
2017
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