Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Restoring Chaos Using Deep Reinforcement Learning   A catastrophic bifurcation in non-linear dynamical systems, called crisis,
often leads to their convergence to an undesirable non-chaotic state after some
initial chaotic transients. Preventing such behavior has proved to be quite
challenging. We demonstrate that deep Reinforcement Learning (RL) is able to
restore chaos in a transiently-chaotic regime of the Lorenz system of
equations. Without requiring any a priori knowledge of the underlying dynamics
of the governing equations, the RL agent discovers an effective perturbation
strategy for sustaining the chaotic trajectory. We analyze the agent's
autonomous control-decisions, and identify and implement a simple control-law
that successfully restores chaos in the Lorenz system. Our results demonstrate
the utility of using deep RL for controlling the occurrence of catastrophes and
extreme-events in non-linear dynamical systems.

18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int