A Deep Q-Network with Experience Optimization (DQN-EO) for Atari’s Space Invaders

  • Yan Chen
  • Elis KullaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


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.


Artificial intelligence Deep Reinforcement Learning Atari Space Invaders Experience Optimization 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Okayama University of ScienceOkayamaJapan

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