  In this study, we address the issue of enabling an artificial intelligence
agent to execute complex language instructions within virtual environments. In
our framework, we assume that these instructions involve intricate linguistic
structures and multiple interdependent tasks that must be navigated
successfully to achieve the desired outcomes. To effectively manage these
complexities, we propose a hierarchical framework that combines the deep
language comprehension of large language models with the adaptive
action-execution capabilities of reinforcement learning agents. The language
module (based on LLM) translates the language instruction into a high-level
action plan, which is then executed by a pre-trained reinforcement learning
agent. We have demonstrated the effectiveness of our approach in two different
environments: in IGLU, where agents are instructed to build structures, and in
Crafter, where agents perform tasks and interact with objects in the
surrounding environment according to language commands.
