FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement
  Learning Techniques
  Reinforcement learning is one of the most popular approaches for automated
game playing. This method allows an agent to estimate the expected utility of
its state in order to make optimal actions in an unknown environment. We seek
to apply reinforcement learning algorithms to the game Flappy Bird. We
implement SARSA and Q-Learning with some modifications such as
$\epsilon$-greedy policy, discretization and backward updates. We find that
SARSA and Q-Learning outperform the baseline, regularly achieving scores of
1400+, with the highest in-game score of 2069.
