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BAT Q-LEARNING ALGORITHM
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نویسنده
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abed-alguni bilal h.
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منبع
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jordanian journal of computers and information technology - 2017 - دوره : 3 - شماره : 1 - صفحه:51 -70
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چکیده
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Cooperative q-learning approach allows multiple learners to learn independently and then share their q-values among each other using a q-value sharing strategy. a main problem with this approach is that the solutions of the learners may not converge to optimality, because the optimal q-values may not be found. another problem is that some cooperative algorithms perform very well with single-task problems, but quite poorly with multi-task problems. this paper proposes a new cooperative q-learning algorithm called the bat q-learning algorithm (bq-learning) that implements a q-value sharing strategy based on the bat algorithm. the bat algorithm is a powerful optimization algorithm that increases the possibility of finding the optimal q-values by balancing between the exploration and exploitation of actions by tuning the parameters of the algorithm. the bq-learning algorithm was tested using two problems: the shortest path problem (single-task problem) and the taxi problem (multi-task problem). the experimental results suggest that bq-learning performs better than single-agent q-learning and some well-known cooperative q-learning algorithms.
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کلیدواژه
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Q-learning ,Bat algorithm ,Optimization ,Cooperative reinforcement learning.
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آدرس
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yarmouk university, computer science department, Jordan
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پست الکترونیکی
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e-mail: bilal.h@yu.edu.jo
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Authors
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