  Flappy Bird, which has a very high popularity, has been trained in many
algorithms. Some of these studies were trained from raw pixel values of game
and some from specific attributes. In this study, the model was trained with
raw game images, which had not been seen before. The trained model has learned
as reinforcement when to make which decision. As an input to the model, the
reward or penalty at the end of each step was returned and the training was
completed. Flappy Bird game was trained with the Reinforcement Learning
algorithm Deep Q-Network and Asynchronous Advantage Actor Critic (A3C)
algorithms.
