This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) using only pixel data as input. We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data. Through behavioural cloning, we examine the ability of individual models to learn varying behavioural traits. We attempt to mimic the behaviour of real players with different play styles, and find we can train agents that behave aggressively, passively, or simply more human-like than traditional AIs. We propose these methods of introducing more depth and human-like behaviour to agents in video games. The trained IL agents perform on par with the average players in our dataset, whilst outperforming the worst players. While performance was not as strong as common RL approaches, it provides much stronger human-like behavioural traits to the agent.

翻译：本文描述了仅使用像素数据作为输入，通过模仿学习训练自主智能体游玩《毁灭战士2》的方法。我们还通过比较相机运动与轨迹数据，探索了强化学习与模仿学习在拟人性方面的差异。通过行为克隆，我们检验了单个模型学习不同行为特征的能力。我们尝试模仿具有不同游戏风格的真实玩家行为，发现可以训练出比传统人工智能更具攻击性、防御性或更接近人类行为的智能体。我们提出这些方法旨在为电子游戏中的智能体引入更丰富的层次与类人行为。经训练的模仿学习智能体在数据集中的表现与普通玩家相当，且优于最差玩家。尽管其性能不及常见的强化学习方案，但能为智能体赋予更强的类人行为特征。