Survey of Deep Neural Networks Handling Plan Development using Simulations of Real-World Environments

dc.contributor.authorAskren, Kaleb
dc.date.accessioned2020-01-06T18:56:13Z
dc.date.available2020-01-06T18:56:13Z
dc.date.issued2019-12-03
dc.description.abstractNeural networks are computational models that demonstrate the capability of advanced computing applications in plan development tasks. Five influential projects that independently demonstrate various applications of neural network models include AlphaGO/AlphaGO Zero, Playing Atari with Deep Reinforcement Learning, NeuroChess, OpenAI Five, and Playing Checkers without Human Expertise. Each of these projects includes different approaches to plan development tasks and are surveyed in this thesis using four criteria: the efficiency of the system, the form of their input based on their target environment, the structure of the neural network(s), and the processes through which they are trained. Each project approaches the plan development problem with strict regard to the environment in which they are targeting and thus vary in implementation. This survey is a collection of the details regarding each project and how the research teams approached their developmenten_US
dc.identifier.urihttps://jewlscholar.mtsu.edu/handle/mtsu/6090
dc.language.isoen_USen_US
dc.publisherUniversity Honors College Middle Tennessee State Universityen_US
dc.subjectBasic and Applied Scienceen_US
dc.subjectNeural networksen_US
dc.subjectAIen_US
dc.subjectmachine learningen_US
dc.subjectstrategy gamesen_US
dc.titleSurvey of Deep Neural Networks Handling Plan Development using Simulations of Real-World Environmentsen_US
dc.typeThesisen_US

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