Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement
  Learning   Goal-conditioned hierarchical reinforcement learning (HRL) has shown
promising results for solving complex and long-horizon RL tasks. However, the
action space of high-level policy in the goal-conditioned HRL is often large,
so it results in poor exploration, leading to inefficiency in training. In this
paper, we present HIerarchical reinforcement learning Guided by Landmarks
(HIGL), a novel framework for training a high-level policy with a reduced
action space guided by landmarks, i.e., promising states to explore. The key
component of HIGL is twofold: (a) sampling landmarks that are informative for
exploration and (b) encouraging the high-level policy to generate a subgoal
towards a selected landmark. For (a), we consider two criteria: coverage of the
entire visited state space (i.e., dispersion of states) and novelty of states
(i.e., prediction error of a state). For (b), we select a landmark as the very
first landmark in the shortest path in a graph whose nodes are landmarks. Our
experiments demonstrate that our framework outperforms prior-arts across a
variety of control tasks, thanks to efficient exploration guided by landmarks.

16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci