A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation

Elis Kulla
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Elis Kulla: Okayama University of Science, Japan

International Journal of Distributed Systems and Technologies (IJDST), 2022, vol. 13, issue 1, 1-13

Abstract: During recent years, the deep Q-Learning is used to solve different complex problems in different fields. However, Deep Q-Learning does not have a unified method for solving certain problems because different problems require specific settings and parameters. This paper proposes a Deep Q-Network with Experience Optimization for Atari’s “Space Invaders” environment called DQN-EO. Training and testing results are presented. The performance evaluation results show that while using the proposed algorithm the agent is better at avoiding enemy bullets by 37.7% (longer lifetime) and destroying enemy ships by 14.5% (higher score).

Date: 2022
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