  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.
