FLAPPY BIRD AUTOMATION USING REINFORCEMENT ALGORITHM - O S Sumukh,; Prinson Fernandes, Merin Meleet , (EPRA International Journal of Multidisciplinary Research (IJMR), 2022)
One of the most popular subjects being investigated in AI nowadays is game learning. Addressing such issues require proper domain specific knowledge.So one such game was developed ie., flappy bird where the agent learns itself on how to avoid the obstacles and also tries to maximize the score based on the rewards and punishments it receives. No prior knowledge was given to the agent regarding the environment. Instead of utilizing raw pixels, the agent was trained using domain-specific features such as the bird's speed, the distance between pipes, and the height of the pipes, which significantly simplifies the feature space and avoids the need for deeper models to automatically extract underlying data. The agent was trained using the NeuroEvolution of Augmenting Topologies(NEAT) algorithm and is talked about in this paper.