Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning
This addresses the challenge of controlling systems with unknown internal dynamics, such as greenhouses, though it appears incremental as it applies an existing RL method to a specific domain.
The paper tackled the problem of controlling a nonlinear, complex, and black-boxed greenhouse system using an actor-critic reinforcement learning approach, achieving maintenance of the environment at least 20 times longer than PID and Deep Q Learning.
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.