CS229 Project Final Report Deep Q-Learning on Arcade Game Assault

Atari 2600 Assault is a game environment provided on the OpenAI Gym platform; it is a top-down shoot’em up game where the player gains reward points for destroying enemy ships. The enemy consists of a mothership and smaller vessels that shoot at the player. The player can move and shoot in various directions with a total of 7 actions available. Every time the player shoots, a heat meter keeps track of how ‘hot’ the engine is; if the player shoots too frequently, the player can lose a life when the heat meter fills up due to overheating. The player can also lose a life upon taking fire from enemy ships. The game ends when the player runs out of lives. We create an AI agent that generates the optimal actions, taking raw pixels as features by feeding them into a convolutional neural network (CNN), also known as deep Q-learning.