Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-View Reinforcement Learning   This paper is concerned with multi-view reinforcement learning (MVRL), which
allows for decision making when agents share common dynamics but adhere to
different observation models. We define the MVRL framework by extending
partially observable Markov decision processes (POMDPs) to support more than
one observation model and propose two solution methods through observation
augmentation and cross-view policy transfer. We empirically evaluate our method
and demonstrate its effectiveness in a variety of environments. Specifically,
we show reductions in sample complexities and computational time for acquiring
policies that handle multi-view environments.

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The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc