  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.
