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de Bruin, T.D. (author), Kober, J. (author), Tuyls, Karl (author), Babuska, R. (author)Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient...journal article 2020
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Alibekov, Eduard (author), Kubalik, Jiri (author), Babuska, R. (author)This paper addresses the problem of deriving a policy from the value function in the context of critic-only reinforcement learning (RL) in continuous state and action spaces. With continuous-valued states, RL algorithms have to rely on a numerical approximator to represent the value function. Numerical approximation due to its nature virtually...journal article 2018