Reinforcement Learning for Video Games

We present an implementation of a specific type of deep reinforcement learning algorithm known as deep Qlearning. With a Convolutional Neural Network (CNN) combined with our Q-learning algorithm, we trained an agent to play the game of Snake. The input to the CNN is the raw pixel values from the Snake environment and the output is a value function which estimates future rewards for different actions. We implemented the Q-learning algorithm on a grid based and a pixel based representation of the Snake environment and found that the algorithm can perform at human level on smaller grid based representation whilst the performance on the pixel based representation was fairly limited.