  Reinforcement learning has been successful in many tasks ranging from robotic
control, games, energy management etc. In complex real world environments with
sparse rewards and long task horizons, sample efficiency is still a major
challenge. Most complex tasks can be easily decomposed into high-level planning
and low level control. Therefore, it is important to enable agents to leverage
the hierarchical structure and decompose bigger tasks into multiple smaller
sub-tasks. We introduce an approach where we use language to specify sub-tasks
and a high-level planner issues language commands to a low level controller.
The low-level controller executes the sub-tasks based on the language commands.
Our experiments show that this method is able to solve complex long horizon
planning tasks with limited human supervision. Using language has added benefit
of interpretability and ability for expert humans to take over the high-level
planning task and provide language commands if necessary.
