Game AI Agent using Deep Reinforcement Learning
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2020-08-30
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Nowadays, 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.
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North South University