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نتیجه جستجو - Visual Deep Reinforcement Learning

تعداد مقالات یافته شده: 1
ردیف عنوان نوع
1 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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