Restoring Chaos Using Deep Reinforcement Learning

TL;DR

This paper shows that deep reinforcement learning can autonomously restore chaotic behavior in the Lorenz system, preventing undesirable non-chaotic states without prior knowledge of the system.

Contribution

It introduces a deep RL approach to control chaos in non-linear systems, discovering effective perturbation strategies without prior system knowledge.

Findings

Deep RL successfully restores chaos in the Lorenz system.

A simple control law derived from RL decisions effectively maintains chaos.

The method prevents catastrophic bifurcations in non-linear dynamics.

Abstract

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.