How AI Surpassed Humans In Playing Flappy Bird Game

Reinforcement learning has exceeded human-level performance when it comes to playing games. Games as a testbed have rich and challenging domains for testing reinforcement learning algorithms that start with a collection of games and well-known reinforcement learning implementations. Reinforcement learning is beneficial when we need an agent to perform a specific task, but to be precise, there is no single “correct” method of accomplishing it. In a paper, researcher Kevin Chen showed that deep reinforcement learning is very efficient at learning how to operate the game Flappy Bird, despite the high-dimensional sensory input. According to the researcher, the goal of this project is to get a policy to have an agent that can successfully play the bird game. Flappy Bird is a popular m