  Angry Birds is a popular video game in which the player is provided with a
sequence of birds to shoot from a slingshot. The task of the game is to destroy
all green pigs with maximum possible score. Angry Birds appears to be a
difficult task to solve for artificially intelligent agents due to the
sequential decision-making, non-deterministic game environment, enormous state
and action spaces and requirement to differentiate between multiple birds,
their abilities and optimum tapping times. We describe the application of Deep
Reinforcement learning by implementing Double Dueling Deep Q-network to play
Angry Birds game. One of our main goals was to build an agent that is able to
compete with previous participants and humans on the first 21 levels. In order
to do so, we have collected a dataset of game frames that we used to train our
agent on. We present different approaches and settings for DQN agent. We
evaluate our agent using results of the previous participants of AIBirds
competition, results of volunteer human players and present the results of
AIBirds 2018 competition.
