In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasoning abilities of game agents in grounded, language-based environments. Our experimental study indicates that injecting the actions from these symbolic modules into the action space of a behavior cloned transformer agent increases performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by an average of 22%, allowing an agent to reach the highest possible performance on unseen games. This action injection technique is easily extended to new agents, environments, and symbolic modules.

翻译：在这项工作中,我们探索用象征性模块的信息来增强互动代理器的技术,这与人类使用计算器和全球定位系统等工具协助计算和导航非常相似。我们在文本游戏中测试我们代理器的能力 -- -- 在基于语言的环境中评估游戏代理器的多步推理能力有挑战性的基准。我们的实验研究表明,将这些象征性模块的行动注入行为克隆变压器的动作空间可以提高四个文本游戏基准的性能,即平均22%的算术、导航、排序和常识推理测试,使代理器能够在无形游戏上达到尽可能高的性能。这种行动注入技术很容易推广到新的代理器、环境和象征性模块。