论文标题
对基于文本游戏的深入强化学习代理的分析
An Analysis of Deep Reinforcement Learning Agents for Text-based Games
论文作者
论文摘要
基于文本的游戏（TBG）是复杂的环境，允许用户或计算机代理进行文本交互并实现游戏目标。在TBG代理设计和培训过程中，平衡代理模型的效率和性能是一个重大挑战。在标准化环境中找到TBG代理深度学习模块的性能，并且在不同评估类型中测试其性能对于TBG代理研究也很重要。我们构建了一个标准化的TBG代理，没有手工制作的规则，正式对TBG评估类型进行了分类，并在我们的环境中分析了选定的方法。
Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals.In TBG agent design and training process, balancing the efficiency and performance of the agent models is a major challenge. Finding TBG agent deep learning modules' performance in standardized environments, and testing their performance among different evaluation types is also important for TBG agent research. We constructed a standardized TBG agent with no hand-crafted rules, formally categorized TBG evaluation types, and analyzed selected methods in our environment.
