  The adoption of pre-trained language models to generate action plans for
embodied agents is a promising research strategy. However, execution of
instructions in real or simulated environments requires verification of the
feasibility of actions as well as their relevance to the completion of a goal.
We propose a new method that combines a language model and reinforcement
learning for the task of building objects in a Minecraft-like environment
according to the natural language instructions. Our method first generates a
set of consistently achievable sub-goals from the instructions and then
completes associated sub-tasks with a pre-trained RL policy. The proposed
method formed the RL baseline at the IGLU 2022 competition.
