Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Continuous Control Policies by Stochastic Value Gradients   We present a unified framework for learning continuous control policies using
backpropagation. It supports stochastic control by treating stochasticity in
the Bellman equation as a deterministic function of exogenous noise. The
product is a spectrum of general policy gradient algorithms that range from
model-free methods with value functions to model-based methods without value
functions. We use learned models but only require observations from the
environment in- stead of observations from model-predicted trajectories,
minimizing the impact of compounded model errors. We apply these algorithms
first to a toy stochastic control problem and then to several physics-based
control problems in simulation. One of these variants, SVG(1), shows the
effectiveness of learning models, value functions, and policies simultaneously
in continuous domains.

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The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph