Abstract
In this paper, Profit Sharing using convolutional neural network is realized. In the proposed method, action value in Profit Sharing is learned by convolutional neural network. This is a method that learns the value function of Profit Sharing instead of the value function of Q Learning used in the Deep Q-Network. By changing to an error function based on the value function of Profit Sharing which can acquire probabilistic policy in a shorter time, the proposed method is able to learn in a shorter time than the conventional Deep Q-Network. Computer experiments were carried out on Asterix of Atari 2600, and the proposed method was compared with the conventional Deep Q-Network. As a result, we confirmed that the proposed method can learn from the earlier stage than Deep Q-Network and can obtain higher score finally.
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Hasuike, N., Osana, Y. (2018). Learning Game by Profit Sharing Using Convolutional Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_5
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DOI: https://doi.org/10.1007/978-3-030-01418-6_5
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