Extracting features from raw data such as videos and soundtracks is difficult and almost impossible. Handcrafted design and feature engineering are hard tasks and need experimentation, evaluation, and creativity. Feature engineering requires exploring the data and the impact of each feature before selecting them. This exploration process requires significant time and effort. Deep learning is a powerful approach to extracting features by transforming raw data into numerical features. This process occurs by using nodes known as neural networks such as the human brain. Neural networks contain multiple layers, and each layer can extract several features from the input data. In this chapter, the convolutional neural network is used to extract features from video games. These features describe the state or observation of the game to enable an intelligent agent to make decisions. The deep learning algorithm combined with the reinforcement learning approach to build a deep reinforcement learning agent able to play different games using game screenshots and game scores just like a human player. The results showed that the agent is able to enhance its performance after several steps, which proves the efficiency of feature extraction using deep learning algorithms. © 2024 Elsevier Inc. All rights reserved.