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Reinforcement learning is one of the most popular approaches for automatedgame playing. This method allows an agent to estimate the expected utility ofits state in order to make optimal actions in an unknown environment. We seekto apply reinforcement learning algorithms to the game Flappy Bird. Weimplement SARSA and Q-Learning with some modifications such as$\epsilon$-greedy policy, discretization and backward updates. We find thatSARSA and Q-Learning outperform the baseline, regularly achieving scores of1400+, with the highest in-game score of 2069.

FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques