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Reinforcement Learning for Procedural Game Animation: Creating Uncanny Zombie Movements

This thesis explores the use of reinforcement learning within the Unity ML Agents framework to simulate zombie-like movements in humanoid ragdolls. The generated locomotion aims to embrace the Uncanny Valley phenomenon, partly through the way it walks, but also through limb disablement. Additionally, the paper strives to test the effectiveness of reinforcement learning as a valuable tool for generative adaptive locomotion. The research implements reward functions and addresses technical challenges. It lays a focus on adaptability through the limb disablement system. A user study comparing the reinforcement learning agent to Mixamo animations evaluates the effectiveness of simulating zombie-like movements as well as if the Uncanny Valley phenomenon was achieved. Results show that while the reinforcement learning agent may lack believability and uncanniness when compared to the Mixamo animation, it features a level of adaptability that is worth expanding upon. Given the inconclusive results, there is room for further research on the topic to achieve the Uncanny Valley effect and enhance zombie-like locomotion with reinforcement learning.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-68996
Date January 2024
CreatorsTayeh, Adrian, Almquist, Arvid
PublisherMalmö universitet, Institutionen för datavetenskap och medieteknik (DVMT)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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