VizDoom中的行为克隆
Behavioural Cloning in VizDoom
AI总结：
本文研究仅用像素数据通过模仿学习训练智能体游玩《毁灭战士2》，发现行为克隆能赋予智能体比强化学习更强的人类相似行为特征。
摘要：
本文描述了仅使用像素数据作为输入，通过模仿学习（IL）训练自主智能体游玩《毁灭战士2》（Doom 2）游戏的方法。我们还通过比较相机移动和轨迹数据，探讨了强化学习（RL）与IL在人类相似度上的比较。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的行为，并发现我们可以训练出表现得具有攻击性、被动，或者比传统AI更具人类相似度的智能体。我们提出这些方法以为电子游戏中的智能体引入更多深度和类人行为。训练的IL智能体表现与我们数据集中普通玩家持平，同时优于表现最差的玩家。虽然性能不如常见的RL方法，但它为智能体提供了更强的人类相似行为特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
