A Research on Machine Learning Agent in Rogue-like game

Abstract

Collecting large amounts of data in the real world is expensive and has clear limitations. Simulation-generated environments, on the other hand, offer the opportunity to efficiently generate the necessary data and to try different things easily and quickly. In this research, we utilized one of the tools that addresses these challenges, by the Unity Machine Learning tool, to study an efficient automation model that responds to the characteristics of the rogue-like genre. For testing purposes, we implemented a simple game, implanted an agent into the main character of the game, and fed the agent with code to shoot and avoid hostile. The implemented ML Agent successfully recognized the hostile targets and responded by shooting and dodging them. However, instead of learning to prioritize the hostile targets over time by reinforcing itself and shooting the high-risk targets first, it consistently fired in only one of the 360-degree directions given to it at the beginning, which we didn’t expected, so we improved the code. By utilizing the RayPerceptionSensor-Component2D element to directly feed the agent's sensors with information about hostile targets, we found that the agent was able to utilize its ray sensor to detect them and make much more precise aimed shots. In fact, it outperformed the original model by an average of 3.81x, proving that Unity ML Agentcan collect data through reinforcement learning in the roguelike genre.