Implementation of Reinforcement Learning in 2D Based Games Using Open AI Gym

Abstract

Playing games is one of the activities used as a means of entertainment to get fun, fill spare time, or just for light exercise. For these reasons, playing games is very popular with people around the world. Seeing how enthusiastic the community is about games, developers are competing to present games that attract attention. One way is by embedding Artificial Intelligence. Artificial intelligence can be defined as a branch of science that models human thinking. One type of Machine Learning that is closest to the definition of AI is Reinforcement Learning (RL). The basic concept of RL is how to make machines/agents smart after interacting with their environment. In this study, the author tries to implement Reinforcement Learning into one of the Atari games (Breakout) using Open Ai Gym. The purpose of this research is to see how Reinforcement Learning can be implemented into Breakout video games. Based on the results obtained from the test, it is concluded that by implementing the Reinforcement Learning algorithm, agents in the breakout game can get an average reward of 18 with a rate of exploration of 0.02 through more than 30000 episodes. With these results, it can also be said that the Reinforcement Learning algorithm can make agents in breakout games able to play themselves, which can be used to create a walkthrough feature in the game. So that players can learn by seeing how the AI plays through this walkthrough.