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KÖKEN, Özlem Salehi

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Özlem Salehi

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KÖKEN
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    Language inference with multi-head automata through reinforcement learning
    (IEEE, 2020) Şekerci, Alper; Köken, Özlem Salehi; Computer Science; KÖKEN, Özlem Salehi; Şekerci, Alper
    The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple heads, and six different languages are formulated as reinforcement learning problems. Two different algorithms are used for optimization. First algorithm is Q-learning which trains gated recurrent units to learn optimal policies. The second one is genetic algorithm which searches for the optimal solution by using evolution-inspired operations. The results show that genetic algorithm performs better than Q-learning algorithm in general but Q-learning algorithm finds solutions faster for regular languages.