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

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

Elis Kulla
Copyright: © 2022 |Pages: 13
DOI: 10.4018/IJDST.296249
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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).
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Machine learning techniques have been frequently used in various fields recently. Most of the medical applications consist in verifying, identifying or detecting disorders and symptoms on patients. Sasikala, 2021 gives an overview of deep learning algorithms used in medical imaging. In (Majhi, 2020), the authors claim that deep learning is very useful in identifying neuronal disorders based on the patterns observed in symptoms. Other researchers have used deep learning to identify diabetes (Jebran, 2021) and lung cancer (Abidi, 2021) and so on.

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