Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe Exploration in Model-based Reinforcement Learning using Control
  Barrier Functions   This paper develops a model based reinforcement learning (MBRL) framework for
learning online the value function of an infinite-horizon optimal control
problem while obeying safety constraints expressed as control barrier functions
(CBFs). Our approach is facilitated by the development of a novel class of
CBFs, termed Lyapunov-like CBFs (LCBFs), that retain the beneficial properties
of CBFs for developing minimally-invasive safe control policies while also
possessing desirable Lyapunov-like qualities such as positive
semi-definiteness. We show how these LCBFs can be used to augment a
learning-based control policy to guarantee safety and then leverage this
approach to develop a safe exploration framework in a MBRL setting. We
demonstrate that our developed approach can handle more general safety
constraints than comparative methods through a variety of numerical examples.

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The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex