Fuzzy Model-Based Reinforcement Learning
نویسندگان
چکیده
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 with continuous state and action spaces. The output of the algorithm is a TakagiSugeno fuzzy system with linear terms in the consequents of the rules. From the Q-function approximated by this fuzzy system an optimal control strategy can be easily derived. The proposed method is applied to the problem of selecting optimal framework signal plans in urban traffic networks. It is shown that the method outperforms existing model-based approaches.
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