wenqijiang/deep-reinforcement-learning-for-atari-games

This project teaches a computer program to play Breakout, the classic Atari game where you bounce a ball off a paddle to break bricks. Instead of programming the rules directly, the authors let the software learn through trial and error, figuring out on its own how to move the paddle to score points. The repository compares four different learning strategies (Deep Q Learning, Deep SARSA, Double DQN, and Dueling DQN) combined with two different ways of processing the game screen visually (LeNet and VGG-16). Think of it as testing which coaching method and which set of eyes help the program learn fastest within a fixed training period. The code lives in Jupyter notebooks, and a detailed report breaks down what each approach achieved. Someone who would find this useful is a student or hobbyist exploring reinforcement learning for the first time and wanting a concrete, side-by-side comparison of popular algorithms on a well-known game. For example, if you are wondering whether Double DQN is worth the extra complexity over plain DQN, the report's findings give you a practical answer based on actual runs rather than theory. The most notable takeaway is that the simplest visual system (LeNet) paired with Dueling DQN performed best, while the more complex VGG-16 system struggled because it needed far more training time than the authors had compute power for. The project is honest about its limitations: none of the models trained long enough to fully converge, so the results are a snapshot of early learning rather than peak performance. That transparency makes it a useful reference for understanding tradeoffs between model complexity and available computing resources.