Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributional Multivariate Policy Evaluation and Exploration with the
  Bellman GAN   The recently proposed distributional approach to reinforcement learning
(DiRL) is centered on learning the distribution of the reward-to-go, often
referred to as the value distribution. In this work, we show that the
distributional Bellman equation, which drives DiRL methods, is equivalent to a
generative adversarial network (GAN) model. In this formulation, DiRL can be
seen as learning a deep generative model of the value distribution, driven by
the discrepancy between the distribution of the current value, and the
distribution of the sum of current reward and next value. We use this insight
to propose a GAN-based approach to DiRL, which leverages the strengths of GANs
in learning distributions of high-dimensional data. In particular, we show that
our GAN approach can be used for DiRL with multivariate rewards, an important
setting which cannot be tackled with prior methods. The multivariate setting
also allows us to unify learning the distribution of values and state
transitions, and we exploit this idea to devise a novel exploration method that
is driven by the discrepancy in estimating both values and states.

30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph