Deep Reinforcement Learning for Snake

Abstract: The world has recently seen a large increase in both research and development and layman use of machine learning. Machine learning has a broad application domain, e.g, in marketing, production and finance. Although these applications have a predetermined set of rules or goals, this project deals with another aspect of machine learning which is general intelligence. During the course of the project a non-human player (known as agent) will learn how to play the game SNAKE without any outside influence or knowledge of the environment dynamics. After having the agent train for 66 hours and almost two million games an average of 16 points per game out of 35 possible were reached. This is realized by the use of reinforcement learning and deep convolutional neural networks (CNN).