Learning a hierarchy

Researchers have developed a hierarchical reinforcement learning algorithm that learns high-level actions to efficiently solve complex tasks requiring thousands of timesteps. The algorithm was successfully applied to navigation problems, where it discovered high-level actions for walking and crawling in different directions, enabling rapid mastery of new navigation tasks.