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Playing first-person shooter games with machine learning techniques and methods using the VizDoom Game-AI research platform
انجام بازی های شوتر اول شخص با تکنیک ها و روش های یادگیری ماشین با استفاده از بستر تحقیقاتی VizDoom Game-AI-2020 Artificial Intelligence in the form of machine learning is employed in games to control non-human computerplayers,
agents or bots. However, most of these games such as Atari took place in 2D environments that were not
fully observable to the agents. Currently, it is of extreme significance to employ such machine learning techniques
and methods in 3D environments such as Doom. Therefore, In this paper, we train agents on the health
gathering scenario of the classical first-person shooter game Doom by first presenting the Direct Future
Prediction to train an agent that uses a simple architecture with no additional supervisory signals, then differentiate
and compare the performance of the agents trained by using several different machine learning techniques,
and the AI reinforcement learning platform ‘VizDoom’, a 3D partially observable environment, with
interesting enhanced properties that makes agents to stand out from inbuilt AI agents and human players. We
have continued to use computer games as a benchmark for the performance of AI as having been so successful in
the past. We also compared the results of our findings to conclude the performance of the agents trained with
different machine learning techniques. The agents performed well against both human players and inbuilt game
agents. Keywords: Artificial Intelligence | Artificial Neural Network | Autonomous Systems | Computational Intelligence | Intelligent agents | Visual Deep Reinforcement Learning | Machine Learning |
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