Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EPOpt: Learning Robust Neural Network Policies Using Model Ensembles   Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are
represented using rich function approximators like deep neural networks.
Model-based methods where the real-world target domain is approximated using a
simulated source domain provide an avenue to tackle the above challenges by
augmenting real data with simulated data. However, discrepancies between the
simulated source domain and the target domain pose a challenge for simulated
training. We introduce the EPOpt algorithm, which uses an ensemble of simulated
source domains and a form of adversarial training to learn policies that are
robust and generalize to a broad range of possible target domains, including
unmodeled effects. Further, the probability distribution over source domains in
the ensemble can be adapted using data from target domain and approximate
Bayesian methods, to progressively make it a better approximation. Thus,
learning on a model ensemble, along with source domain adaptation, provides the
benefit of both robustness and learning/adaptation.

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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas