This project implements the Deep Q-Learning algorithm based on the Playing Atari with Deep Reinforcement Learning paper. It uses a convolutional neural network that takes raw pixel input and outputs a value function estimating future rewards. The agent learns through Q-learning with experience replay, selecting actions via an epsilon-greedy policy. It demonstrates that deep reinforcement learning can generalize to game-playing tasks like Flappy Bird.