user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning World Graph Decompositions To Accelerate Reinforcement Learning

Paper content:
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them
.  Our framework has two learning phases
: 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and
2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions.
We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.
Many real-world applications, e.g., self-driving cars and in-home robotics, require an autonomous agent to execute different tasks within a single environment that features, e.g. high-dimensional state space, complex world dynamics or structured layouts.
In these settings, model-free reinforcement learning (RL) agents often struggle to learn efficiently, requiring a large amount of experience collections to converge to optimal behaviors.
Intuitively, an agent could learn more efficiently by focusing its exploration in task-relevant regions, if it has knowledge of the high-level structure of the environment.
We propose a method to
1) learn and
2) use an environment decomposition in the form of a world graph, a task-agnostic abstraction.
World graph nodes are waypoint states, a set of salient states that can summarize agent trajectories and provide meaningful starting points for efficient exploration (Chatzigiorgaki & Skodras, 2009; Jayaraman et al., 2018; Ghosh et al., 2018) .
The directed and weighted world graph edges characterize feasible traversals among the waypoints.
To leverage the world graph, we model hierarchical RL (HRL) agents where a high-level policy chooses a waypoint state as a goal to guide exploration towards task-relevant regions, and a low-level policy strives to reach the chosen goals.
Our framework consists of two phases.
In the task-agnostic phase, we obtain world graphs by training a recurrent variational auto-encoder (VAE) (Chung et al., 2015; Gregor et al., 2015; Kingma & Welling, 2013) with binary latent variables (Nalisnick & Smyth, 2016) over trajectories collected using a random walk policy (Ha & Schmidhuber, 2018 ) and a curiosity-driven goal-conditioned policy (Ghosh et al., 2018; Nair et al., 2018) .
World graph nodes are states that are most frequently selected by the binary latent variables, while edges are inferred from empirical transition statistics between neighboring waypoints.
In the task-specific phase, taking advantage of the learned world graph for structured exploration, we efficiently train an HRL model (Taylor & Stone, 2009 ).
In summary, our main contributions are:
• A task-agnostic unsupervised approach to learn world graphs, using a recurrent VAE with binary latent variables and a curiosity-driven goal-conditioned policy.
• An HRL scheme for the task-specific phase that features multi-goal selection (Wide-thenNarrow) and navigation via world graph traversal.
4. On its traversal course to wide goal, agent hits final target and exits.
: waypoints selected by the manager : waypoints initiates traversal : trajectories directly from worker actions : exit point : agent : final goal from manager close to selected waypoints : trajectories from world graph traversal
We have shown that world graphs are powerful environment abstractions, which, in particular, are capable of accelerating reinforcement learning.
Future works may extend their applications to more challenging RL setups, such as real-world multi-task learning and navigation.
It is also interesting to generalize the proposed framework to learn dynamic world graphs for evolving environments, and applying world graphs to multi-agent problems, where agents become part of the world graphs of other agents.

assistant: text: We learn a task-agnostic world graph abstraction of the environment and show how using it for structured exploration can significantly accelerate downstream task-specific RL.