Game AI Agent using Deep Reinforcement Learning

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dc.contributor.advisorDr. Shahnewaz Siddique
dc.contributor.authorMd. Rafat Rahman Tushar
dc.contributor.authorFariha Islam Faiza
dc.contributor.authorMd. Nuruzzaman
dc.contributor.authorAnas Uddin
dc.contributor.id1621450042
dc.contributor.id1620518042
dc.contributor.id1621557042
dc.contributor.id1520143042
dc.date.accessioned2025-12-14
dc.date.accessioned2025-12-14T05:23:45Z
dc.date.available2025-12-14T05:23:45Z
dc.date.issued2020-08-30
dc.description.abstractNowadays, the field of deep learning is expanding very fast. We can use deep learning strategies in almost all of the decision making, intelligence problems as well as searching from insanely big amounts of data. In this project, we present an AI agent that learns how to play games from visionary input data. We, humans, also play games by seeing the screen where the game is being played. Our games have limited discrete action space. Our goal is to train an AI agent that has no previous idea of the games’ environments but can successfully learn the strategies of playing games and get scores same as humans. Even we have managed to gain scores more than humans. Our model successfully learns how to play games effectively and in a short amount of time. As we are working with image data, we have to consider the difficulties of processing and training that huge amount of data. Eventually, by implementing a model based on some deep learning strategies, we were able to reach our goal.
dc.description.degreeUndergraduate
dc.identifier.cd600000284
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1535
dc.language.isoen_US
dc.publisherNorth South University
dc.rights@ NSU Library
dc.titleGame AI Agent using Deep Reinforcement Learning
oaire.citation.endPage62
oaire.citation.startPage1
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