Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning with Prototypical Representations   Learning effective representations in image-based environments is crucial for
sample efficient Reinforcement Learning (RL). Unfortunately, in RL,
representation learning is confounded with the exploratory experience of the
agent -- learning a useful representation requires diverse data, while
effective exploration is only possible with coherent representations.
Furthermore, we would like to learn representations that not only generalize
across tasks but also accelerate downstream exploration for efficient
task-specific training. To address these challenges we propose Proto-RL, a
self-supervised framework that ties representation learning with exploration
through prototypical representations. These prototypes simultaneously serve as
a summarization of the exploratory experience of an agent as well as a basis
for representing observations. We pre-train these task-agnostic representations
and prototypes on environments without downstream task information. This
enables state-of-the-art downstream policy learning on a set of difficult
continuous control tasks.

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The categories of this paper are: atom-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat