Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-Dimensional State and Action Representation Learning with MDP
  Homomorphism Metrics   Deep Reinforcement Learning has shown its ability in solving complicated
problems directly from high-dimensional observations. However, in end-to-end
settings, Reinforcement Learning algorithms are not sample-efficient and
requires long training times and quantities of data. In this work, we proposed
a framework for sample-efficient Reinforcement Learning that take advantage of
state and action representations to transform a high-dimensional problem into a
low-dimensional one. Moreover, we seek to find the optimal policy mapping
latent states to latent actions. Because now the policy is learned on abstract
representations, we enforce, using auxiliary loss functions, the lifting of
such policy to the original problem domain. Results show that the novel
framework can efficiently learn low-dimensional and interpretable state and
action representations and the optimal latent policy.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio