Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals. It is challenging to build goal-oriented computer agents for text-based games, especially when we use step-wise feedback as the only text input for the model. Moreover, it is hard for agents to provide replies with flexible length and form by valuing from a much larger text input space. In this paper, we provide an extensive analysis of deep learning methods applied to the Text-Based Games field.
翻译:以文字为基础的游戏(TBG)是复杂的环境,用户或计算机代理商可以进行文字互动并实现游戏目标。为以文字为基础的游戏建立面向目标的计算机代理商是一项艰巨的任务,特别是当我们使用渐进式反馈作为模型的唯一文本输入时。此外,代理商很难通过从更大的文字输入空间中估价灵活地提供篇幅和形式的答复。在本文中,我们广泛分析了适用于以文字为基础的运动场的深层次学习方法。