Interactive Hierarchical Guidance using Language
This addresses sample efficiency and interpretability issues in reinforcement learning for domains like robotics and games, though it appears incremental as it builds on hierarchical methods by integrating language.
The paper tackles the challenge of sample efficiency in reinforcement learning for complex, long-horizon tasks by introducing a hierarchical approach that uses language to specify sub-tasks, enabling agents to decompose tasks and improve planning with limited human supervision.
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.