Paper Title
Implementation of A Game Using Deep Reinforcement Learning: Snake Game

Deep Reinforcement Learning (DRL) has become a normally adopted methodology to alter the agents to be told complex management policies in varied video games, after Deep-Mind used this technique to play Atari games. However, similar approaches should get to be improved when applied to tougher scenarios, where reward signals are sparse and delayed. In this paper, we will develop a Deep Reinforcement Learning Model along with Deep Q-Learning Algorithm that will enable our autonomous agent to play the classical snake game. Specifically, we will employ a Deep Neural Network (DNN) trained with a variant of Q-Learning. No rules about the game are mentioned, and initially the agent is provided with no information on what it needs to do. The goal for the system is to figure out the rules and elaborate a method to maximize the score or reward. Keywords - Deep reinforcement learning, Q-Learning, Deep Neural Network, Deep Learning.