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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
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
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