Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Planning from Pixels in Atari with Learned Symbolic Representations   Width-based planning methods have been shown to yield state-of-the-art
performance in the Atari 2600 domain using pixel input. One successful
approach, RolloutIW, represents states with the B-PROST boolean feature set. An
augmented version of RolloutIW, $\pi$-IW, shows that learned features can be
competitive with handcrafted ones for width-based search. In this paper, we
leverage variational autoencoders (VAEs) to learn features directly from pixels
in a principled manner, and without supervision. The inference model of the
trained VAEs extracts boolean features from pixels, and RolloutIW plans with
these features. The resulting combination outperforms the original RolloutIW
and human professional play on Atari 2600 and drastically reduces the size of
the feature set.

13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio