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:

Representation learning is a central challenge across a range of machine learning areas.
In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.
Most prior work on representation learning has focused on generative approaches, learning representations that capture all the underlying factors of variation in the observation space in a more disentangled or well-ordered manner.
In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable".
These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, eliminating the need for explicit reconstruction.
We show how these learned representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks.
We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.
Representation learning refers to a transformation of an observation, such as a camera image or state observation, into a form that is easier to manipulate to deduce a desired output or perform a downstream task, such as prediction or control.
In reinforcement learning (RL) in particular, effective representations are ones that enable generalizable controllers to be learned quickly for challenging and temporally extended tasks.
While end-to-end representation learning with full supervision has proven effective in many scenarios, from supervised image recognition BID21 to vision-based robotic control , devising representation learning methods that can use unlabeled data or experience effectively remains an open problem.Much of the prior work on representation learning in RL has focused on generative approaches.
Learning these models is often challenging because of the need to model the interactions of all elements of the state.
We instead aim to learn functionally salient representations: representations that are not necessarily complete in capturing all factors of variation in the observation space, but rather aim to capture factors of variation that are relevant for decision making -that are actionable.How can we learn a representation that is aware of the dynamical structure of the environment?
We propose that a basic understanding of the world can be obtained from a goal-conditioned policy, a policy that can knows how to reach arbitrary goal states from a given state.
Learning how to execute shortest paths between all pairs of states suggests a deep understanding of the environment dynamics, and we hypothesize that a representation incorporating the knowledge of a goal-conditioned policy can be readily used to accomplish more complex tasks.
However, such a policy does not provide a readily usable state representation, and it remains to choose how an effective state representation should be extracted.
We want to extract those factors of the state observation that are critical for deciding which action to take.
We can do this by comparing which actions a goal-conditioned policy takes for two different goal states.
Intuitively, if two goal states require different actions, then they are functionally different and vice-versa.
This principle is illustrated in the diagram in Figure 1 .
Based on this principle, we propose actionable representations for control (ARC), representations in which Euclidean distances between states correspond to expected differences between actions taken to reach them.
Such representations emphasize factors in the state that induce significant differences in the corresponding actions, and de-emphasize those features that are irrelevant for control.Figure 1: Actionable representations: 3 houses A, B, C can only be reached by indicated roads.
The actions taken to reach A, B, C are shown by arrows.
Although A, B are very close in space, they are functionally different.
The car has to take a completely different road to reach A, compared to B and C. Representations z A , z B , z C learn these functional differences to differentiate A from B and C, while keeping B and C close.While learning a goal-conditioned policy to extract such a representation might itself represent a daunting task, it is worth noting that such a policy can be learned without any knowledge of downstream tasks, simply through unsupervised exploration of the environment.
It is reasonable to postulate that, without active exploration, no representation learning method can possibly acquire a dynamics-aware representation, since understanding the dynamics requires experiencing transitions and interactions, rather than just observations of valid states.
As we demonstrate in our experiments, representations extracted from goal-conditioned policies can be used to better learn more challenging tasks than simple goal reaching, which cannot be easily contextualized by goal states.
The process of learning goal-conditioned policies can also be made recursive, so that the actionable representations learned from one goalconditioned policy can be used to quickly learn a better one.Actionable representations for control are useful for a number of downstream tasks: as representations for task-specific policies, as representations for hierarchical RL, and to construct well-shaped reward functions.
We show that ARCs enable these applications better than representations that are learned using unsupervised generative models, predictive models, and other prior representation learning methods.
We analyze structure of the learned representation, and compare the performance of ARC with a number of prior methods on downstream tasks in simulated robotic domains such as wheeled locomotion, legged locomotion, and robotic manipulation.
In this work, we introduce actionable representations for control (ARC), which capture representations of state important for decision making.
We build on the framework of goal-conditioned RL to extract state representations that emphasize features of state that are functionally relevant.
The learned state representations are implicitly aware of the dynamics, and capture meaningful distances in representation space.
ARCs are useful for tasks such as learning policies, HRL and exploration.
While ARC are learned by first training a goal-conditioned policy, learning this policy using offpolicy data is a promising direction for future work.
Interleaving the process of representation learning and learning of the goal-conditioned policy promises to scale ARC to more general tasks.A EXPERIMENTAL DETAILS
Learning state representations which capture factors necessary for control