SOTAVerified

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

2019-07-30Code Available0· sign in to hype

Byunghyun Ban, Soobin Kim

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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.

Tasks

Reproductions