Learning game by profit sharing using convolutional neural network

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.