Abstract
Since Deep Q-Learning was introduced in 2013, a lot of academia and industry researchers, tried to use it to solve their problems, in different fields. However, Deep Q-Learning does not consist of a unified method for solving certain problems. In contrary, every problem requires specific settings and parameters. In this paper, we propose a Deep Q-Network with Experience Optimization, for Atari’s “Space Invaders” environment. Training and Testing results show that, while using our proposed network, there is a strong correlation between the key action (shoot) and scoring.
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References
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Chen, Y., Kulla, E. (2019). A Deep Q-Network with Experience Optimization (DQN-EO) for Atari’s Space Invaders. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_33
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DOI: https://doi.org/10.1007/978-3-030-15035-8_33
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