Machine learning in Unity

Abstract

U ovom diplomskom radu prikazuju se teorijske i praktične mogućnosti Unityj-a u izradi simulacije za učenje inteligentnih agenata algoritmima strojnog učenje. Unity je game engine koji se koristi za razvoj 2D i 3D videoigara, simulacija i interaktivnog sadržaja, a ML-Agents je plugin za Unity koji omogućuje izradu simulacija u kojima se koristi strojno učenje za treniranje inteligentnih agenata u obavljanju određenih zadataka. Strojno učenje obuhvaća metode koje omogućuje računalu da „uči“ obavljati određeni zadatak pomoću unesenih podataka, a bez eksplicitnog programiranja. U sklopu rada izrađen je projekt u kojem inteligentni agent uči korištenjem strojnog učenja s potporom. Zadatak agenta je prolaziti nasumično generiranom cestom i doći do njenog kraja. Na cesti se stvaraju prepreke koje agent mora zaobići, isto tako agent mora obaviti zadatak unutar određenog vremena i ne smije izaći iz granica ceste.This graduate thesis presents theoretical and practical possibilites of Unity in making a simulation for traning intelligent agents with machine learning algorithms. Unity is a game engine which is used for developing 2D and 3D games, simulations and interactive content. ML-Agents is a Unity plugin which enables creating simulations that use machine learning algorithms to train intelligent agents in performing certain tasks. Machine learning encompasses methods that enable a computer to „learn“ to perform a particual task using input data, without explicit programming. Within the thesis project is created in which intelligent agent learns by using reinforcement machine learning. Agent has a task to navigate the randomly generated road and get to its end. Obstacles are created on the road which agent has to avoid, also agent has to complete the task within a certain time and must not leave the road boundaries