  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.
