Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive
  Shielding   Reinforcement learning is a promising approach to synthesizing policies for
challenging robotics tasks. A key problem is how to ensure safety of the
learned policy---e.g., that a walking robot does not fall over or that an
autonomous car does not run into an obstacle. We focus on the setting where the
dynamics are known, and the goal is to ensure that a policy trained in
simulation satisfies a given safety constraint. We propose an approach, called
model predictive shielding (MPS), that switches on-the-fly between a learned
policy and a backup policy to ensure safety. We prove that our approach
guarantees safety, and empirically evaluate it on the cart-pole.

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The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con