In the video game industry, developers utilize game engines to bring their creations from concept to reality. However, the most widely used engine in the industry currently is Unreal Engine 5 or UE5, which has challenged established practices used by game developers since the early creation of 3D interactive media. One of these challenges is eliminating LODs or Level of Detail-based asset integration with the introduction of Nanite, an automatic LOD creation tool introduced in Unreal Engine 5. With this development, it is still being determined whether Nanite should immediately replace LODs due to its ability to cut out work required for LOD-based integration. This uncertainty has led to the purpose of this study, which is to research and understand the background and utilization of LODs and Nanite in 3D game asset creation while showcasing how both processes intertwine inside UE5.The following research questions will guide this study in answering questions and setting up foundation knowledge into LODs and Nanite to understand the importance of each optimization technique and why their usage matters to the future of 3D asset creation. First, what are LODs, and why are they used in 3D asset creation within the video game industry? Second, what is Nanite, and how does this process differentiate itself from the methods utilized in LOD creation? Third, what are the benefits and consequences of using either LODs or Nanite in 3D asset creation? Lastly, can both processes be utilized in tandem inside Unreal Engine 5.2.1 to allow developers to use the best abilities of both methods in their 3D asset creation pipelines?

Drönare är ett växande fenomen i dagens samhälle och deras användningsområdenhar snabbt ökat de senaste åren. För att underlätta utvecklingen av drönarteknologi kansimuleringar tillämpas då de möjliggör testning i en kontrollerad och riskfri miljö. I dettaprojekt visualiseras simuleringar av drönare i ett försök att skapa värde för denna utveckling. Visualiseringen skedde med hjälp av Unreal Engine 5. Under projektets gång studerades arbetsmetoderna som nyttjades och hur en systemanatomi kan bidra till utvecklingen iett småskaligt mjukvaruprojekt. Största värdet som producerades för kunden var kommunikationsmodulen. Kommunikationen uppnåddes med hjälp av två JSON-filer som visualiseringen respektive simuleringen skrev till för att kommunicera med varandra. Värdeti denna modul ligger i att kunden sökte en modulär lösning för att kommunicera mellanen 3D-visualisering och en simulering. En erfarenhet som uppmärksammades angåendearbetsmetoderna är vikten av en fungerande gruppdynamik. Bidragande faktorer till detvar agila arbetsmetoder, goda kommunikationsvägar och en väl planerad användning avGit. Systemanatomier upplevdes ge begränsat värde till projektet.

<p dir="ltr">The concept of reinforcement learning has become increasingly relevant in learning- based applications, especially in the field of autonomous navigation, because of its fundamental nature to operate without the necessity of labeled data. However, the infeasibility of training reinforcement learning based autonomous navigation applications in a real-world setting has increased the popularity of researching and developing on autonomous navigation systems by creating simulated environments in game engine platforms. This thesis investigates the comparative performance of Unity and Unreal Engine 5 within the framework of a reinforcement learning system applied to autonomous race car navigation. A rudimentary simulated setting featuring a model car navigating a racetrack is developed, ensuring uniformity in environmental aspects across both Unity and Unreal Engine 5. The research employs reinforcement learning with genetic algorithms to instruct the model car in race track navigation; while the tools and programming methods for implementing reinforcement learning vary between the platforms, the fundamental concept of reinforcement learning via genetic algorithms remains consistent to facilitate meaningful comparisons. The implementation includes logging of key performance variables during run times on each platform. A comparative analysis of the performance data collected demonstrates Unreal Engine's superior performance across the collected variables. These findings contribute insights to the field of autonomous navigation systems development and reinforce the significance of choosing an optimal underlying simulation platform for reinforcement learning applications.</p>