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Implementing Reinforcement Learning in Unreal Engine 4 with Blueprint

Show simple item record Boyd, Reece 2017-05-03T18:18:33Z 2017-05-03T18:18:33Z 2017-05
dc.description.abstract With the availability of modern sophisticated game engines, it has never been easier to create a game for implementing and analyzing machine learning (ML) algorithms. Game engines are useful for academic research because they can produce ideal environments for rapid simulation and provide ways to implement Artificial Intelligence (AI) in Non-Player Characters (NPCs). Unreal Engine 4 (UE4) is a great choice for ML simulation as it contains many useful tools. These tools include Blueprint Visual Scripting that can be converted into performant C++ code, simple-to-use Behavior Trees (BT) for setting up traditional AI, and more complex tools such as AIComponents and the Environment Query System (EQS) for giving an agent the ability to perceive its environment. These built-in tools were used to create a simple, extensible, and open-source environment for implementing ML algorithms in hopes that it will reduce the barrier of entry for using these algorithms in academic and industry-focused research. Experimental results indicate that reinforcement learning (RL) algorithms implemented in Blueprint can lead to learning a successful policy in very short training episodes. en_US
dc.publisher University Honors College, Middle Tennessee State University en_US
dc.subject reinforcement learning en_US
dc.subject unreal engine en_US
dc.subject blueprint en_US
dc.subject machine learning en_US
dc.subject artificial intelligence en_US
dc.subject computer science en_US
dc.title Implementing Reinforcement Learning in Unreal Engine 4 with Blueprint en_US
dc.type Thesis en_US

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