Evaluation of fuzzy rule bases under delayed reinforcement
نویسندگان
چکیده
This article concerns the problem and its solution in judging fuzzy rule bases according to environmental reinforcements. We porpose an on-line, incremental credit assignment algorithm , which takes environmental reinforcement as input and assigns credit to individual rules. The proposed approach adopts simple updating policy based on recency-weighted average, and demands only small amount of memory. We also contribute to the problem of delayed reinforcement. In case of delayed reinforcement , the state preference function is constructed iteratively during the exploration phase.
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