Character Simulation Using Imitation Learning With Game Engine Physics

TL;DR

This paper explores using game engine physics and plugins like ML-Agents in Unity3D to create and simulate 3D sensing characters for interactive gaming and AI training.

Contribution

It demonstrates how game engines can be leveraged to develop and visualize AI agents using off-the-shelf algorithms for game interaction and training.

Findings

Game engines facilitate easy creation of visual AI characters.

Agents can be trained to play games at competitive levels.

Visual simulation enhances AI development and testing.

Abstract

Creating visual 3D sensing characters that interact with AI peers and virtual environments can be a difficult task for those with less experience in using learning algorithms or creating visual environments to execute an agent-based simulation. In this paper, the use of game engines as a tool to create and execute graphic simulations with 3D sensing characters is being explored with plugins such as ML-Agents for the Unity3D game engine. This allows the simulation of agents using off-the-shelf algorithms and using the game engine's motor for the visualizations of these agents. We explore the use of these tools to create visual bots for games, and teach them how to play the game until they reach a level where they can serve as adversaries for real-life players in interactive games.