Model-based reinforcement learning methods are known to be highly efficient with respect to the number of trials required for learning optimal policies. In this article, a novel fuzzy model-based reinforcement learning approach, fuzzy prioritized sweeping (F-PS), is presented. The approach is capable of learning strategies for Markov decision problems withâ€¦ (More)

Model-based reinforcement learning can be applied to problems with continuous state spaces by discretizing the spaces with crisp or fuzzy partitions. The manual definition of suitable partitions, however, is often not trivial, since fine partitions lead to a high number of states and thus complex discrete problems, whereas coarse partitions can beâ€¦ (More)

Reinforcement learning enables machines to learn from experiences. For example, controllers can learn optimal control strategies trying out different strategies and evaluating the resulting performance of the processes under control. At present reinforcement learning is rarely used for the optimization of complex industrial processes, since theâ€¦ (More)