Multidisciplinary Optimization in Decentralized Reinforcement Learning

Multidisciplinary Optimization (MDO) is one of the most popular techniques in aerospace engineering, where the system is complex and includes the knowledge from multiple fields. However, according to the best of our knowledge, MDO has not been widely applied in decentralized reinforcement learning (RL) due to the `unknown' nature of the RL problems. In this work, we apply the MDO in decentralized RL. In our MDO design, each learning agent uses system identification to closely approximate the environment and tackle the `unknown' nature of the RL. Then, the agents apply the MDO principles to compute the control solution using Monte Carlo and Markov Decision Process techniques. We examined two options of MDO designs: the multidisciplinary feasible and the individual discipline feasible options, which are suitable for multi-agent learning. Our results show that the MDO individual discipline feasible option could successfully learn how to control the system. The MDO approach shows better performance than the completely decentralization and centralization approaches