A stabilizing reinforcement learning approach for sampled systems with partially unknown models
Lukas Beckenbach,
Pavel Osinenko,
Stefan Streif
Abstract:Reinforcement learning is commonly associated with training of reward‐maximizing (or cost‐minimizing) agents, in other words, controllers. It can be applied in model‐free or model‐based fashion, using a priori or online collected system data to train involved parametric architectures. In general, online reinforcement learning does not guarantee closed loop stability unless special measures are taken, for instance, through learning constraints or tailored training rules. Particularly promising are hybrids of re… Show more
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