Artificial Intelligence in the form of machine learning is employed in games to control non-human computer-players, 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.

游戏中采用了机器学习形式的人工智能来控制非人类的计算机玩家，代理或机器人。但是，大多数此类游戏（例如Atari）都是在2D环境中进行的，代理商无法完全观察到。当前，在诸如Doom之类的3D环境中采用此类机器学习技术和方法具有极其重要的意义。因此，在本文中，我们通过首先展示直接未来预测来训练使用简单架构且没有其他监督信号的代理来训练代理，以了解经典的第一人称射击游戏《毁灭战士》的健康状况。通过使用几种不同的机器学习技术以及AI强化学习平台“ VizDoom”（一种3D部分可观察的环境）来训练代理的性能，具有有趣的增强属性，使特工从内置的AI特工和人类玩家中脱颖而出。过去，我们一直非常成功地将计算机游戏作为AI性能的基准。我们还比较了我们的发现结果，以得出使用不同机器学习技术训练的代理的性能。代理对人类玩家和内置游戏代理的表现都很好。