Deep Q-Learning with Space Invaders

This post introduces Deep Q-Learning as an extension of Q-Learning for handling large state spaces. It details the Deep Q-Network (DQN) architecture, input preprocessing techniques (resizing, grayscale, frame stacking), and the core Deep Q-Learning algorithm which uses a neural network to approximate Q-values and a loss function with gradient descent for training. Key stabilization techniques like Experience Replay, Fixed Q-Targets, and Double DQN are explained, along with their benefits in improving training efficiency, stability, and reducing Q-value overestimation. The post demonstrates the application of DQN in playing Atari games.