Reinforcement learning is essential for applications where there is no single correct way to solve a problem. We show in this project that we can use reinforcement learning to effectively learn how to play Flappy Bird even when there is high dimensional input. The agent which is the bird in this case, learns the world around it on its own and uses the input to come up with an optimal strategy to navigate through the environment. We use a convolutional neural network with Q-learning for the purpose and we also discuss the potential challenges and improvements that can be made.