Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning

TL;DR

This paper presents a reinforcement learning approach to control complex, nonlinear, and black-box greenhouse systems, outperforming traditional PID and Deep Q Learning methods in maintaining environmental conditions.

Contribution

It introduces an actor-critic reinforcement learning method for controlling black-boxed nonlinear systems, demonstrated on a greenhouse simulator with superior performance.

Findings

Reinforcement learning maintained environment 20 times longer than PID.

Actor-critic approach outperformed Deep Q Learning.

Effective control of complex, black-boxed systems demonstrated.

Abstract

Modern control theories such as systems engineering approaches try to solve nonlinear system problems by revelation of causal relationship or co-relationship among the components; most of those approaches focus on control of sophisticatedly modeled white-boxed systems. We suggest an application of actor-critic reinforcement learning approach to control a nonlinear, complex and black-boxed system. We demonstrated this approach on artificial green-house environment simulator all of whose control inputs have several side effects so human cannot figure out how to control this system easily. Our approach succeeded to maintain the circumstance at least 20 times longer than PID and Deep Q Learning.