Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps.
Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.
We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.
We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.
We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.
We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.
Humans encounter a wide variety of tasks throughout their lives and utilize prior knowledge to master new tasks quickly.
In contrast, reinforcement learning algorithms are typically used to solve each task independently and from scratch, and they require far more experience than humans.
While a large body of research seeks to improve the sample efficiency of reinforcement learning algorithms, there is a limit to learning speed in the absence of prior knowledge.We consider the setting where agents solve distributions of related tasks, with the goal of learning new tasks quickly.
One challenge is that while we want to share information between the different tasks, these tasks have different optimal policies, so it's suboptimal to learn a single shared policy for all tasks.
Addressing this challenge, we propose a model containing a set of shared sub-policies (i.e., motor primitives), which are switched between by task-specific master policies.
This design is closely related to the options framework BID17 BID1 , but applied to the setting of a task distribution.
We propose a method for the end-to-end training of sub-policies that allow for quick learning on new tasks, handled solely by learning a master policy.Our contributions are as follows.•
We formulate an optimization problem that answers the question of what is a good hierarchy?-the
problem is to find a set of low-level motor primitives that enable the high-level master policy to be learned quickly.• We
propose an optimization algorithm that tractably and approximately solves the optimization problem we posed. The
main novelty is in how we repeatedly reset the master policy, which allows us to adapt the sub-policies for fast learning.We will henceforth refer to our proposed method-including the hierarchical architecture and optimization algorithm-as MLSH, for metalearning shared hierarchies.We validate our approach on a wide range of environments, including 2D continuous movement, gridworld navigation, and 3D physics tasks involving the directional movement of robots. In
the 3D environments, we enable humanoid robots to both walk and crawl with the same policy; and 4-legged robots to discover directional movement primitives to solve a distribution of mazes as well as sparse-reward obstacle courses. Our
experiments show that our method is capable of learning meaningful sub-policies solely through interaction with a distributions of tasks, outperforming previously proposed algorithms. We
also display that our method is efficient enough to learn in complex physics environments with long time horizons, and robust enough to transfer sub-policies towards otherwise unsolvable sparse-reward tasks.
In this work, we formulate an approach for the end-to-end metalearning of hierarchical policies.
We present a model for representing shared information as a set of sub-policies.
We then provide a framework for training these models over distributions of environments.
Even though we do not optimize towards the true objective, we achieve significant speedups in learning.
In addition, we naturally discover diverse sub-policies without the need for hand engineering.
learn hierarchal sub-policies through end-to-end training over a distribution of tasks