Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DreamingV2: Reinforcement Learning with Discrete World Models without
  Reconstruction   The present paper proposes a novel reinforcement learning method with world
models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming.
DreamerV2 is a cutting-edge model-based reinforcement learning from pixels that
uses discrete world models to represent latent states with categorical
variables. Dreaming is also a form of reinforcement learning from pixels that
attempts to avoid the autoencoding process in general world model training by
involving a reconstruction-free contrastive learning objective. The proposed
DreamingV2 is a novel approach of adopting both the discrete representation of
DreamingV2 and the reconstruction-free objective of Dreaming. Compared to
DreamerV2 and other recent model-based methods without reconstruction,
DreamingV2 achieves the best scores on five simulated challenging 3D robot arm
tasks. We believe that DreamingV2 will be a reliable solution for robot
learning since its discrete representation is suitable to describe
discontinuous environments, and the reconstruction-free fashion well manages
complex vision observations.

32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph