A review on the application of reinforcement learning to the 2048 game

Abstract

In light of the rapid development of Artificial Intelligence (AI), this paper pries into the application of reinforcement learning on the 2048 game which has important implications on the algorithms ability to solve various real-life problems including but not limited to robotics, auto-driving, and financial trading. The paper summarizes the possible usage of the 2048 model in Q-learning, convolutional neural networks, genetic, and Monte Carlo tree search algorithms. Then the paper scopes into the current progress and methods used in solving the 2048 game with reinforcement learning. Starting off with Q-learning, its implementation and limitations, such as large state space, sparse rewards, limited exploration, computational complexity, and a lack of human-like intuition, are illustrated. Deep Q-learning handles the problem of large state space from Q-learning. However, most of the disadvantages that are found in Q-learning are shared by Deep Q-learning and hence should become the focus of future research on using reinforcement learning to solve 2048.