  Complex systems are ubiquitous in the real world and tend to have complicated
and poorly understood dynamics. For their control issues, the challenge is to
guarantee accuracy, robustness, and generalization in such bloated and troubled
environments. Fortunately, a complex system can be divided into multiple
modular structures that human cognition appears to exploit. Inspired by this
cognition, a novel control method, Causal Coupled Mechanisms (CCMs), is
proposed that explores the cooperation in division and competition in
combination. Our method employs the theory of hierarchical reinforcement
learning (HRL), in which 1) the high-level policy with competitive awareness
divides the whole complex system into multiple functional mechanisms, and 2)
the low-level policy finishes the control task of each mechanism. Specifically
for cooperation, a cascade control module helps the series operation of CCMs,
and a forward coupled reasoning module is used to recover the coupling
information lost in the division process. On both synthetic systems and a
real-world biological regulatory system, the CCM method achieves robust and
state-of-the-art control results even with unpredictable random noise.
Moreover, generalization results show that reusing prepared specialized CCMs
helps to perform well in environments with different confounders and dynamics.
