Reinforcement learned adversarial agent (ReLAA) for active fault detection and prediction in space habitats

Abstract

With growing interest for human space tourism in the twenty-first century, much attention has been directed to the robust engineering of Environmental Control and Life Support Systems in space habitats. The stable, reliable operation of such a habitat is partly achieved with an ability to recognize and predict faults. For these two purposes, a reinforcement learning adversarial agent (ReLAA) is utilized in this work. A ReLAA is trained with experimental data to actively recognize and predict faults. These capabilities are achieved by proposing actions that activate known faults in a system. Instead of issuing these harmful actions to the actual hardware, a digital twin of the mock space habitat is simulated to discover vulnerabilities that would lead to faulted operation. The methods developed in this work will allow for the discovery of damaging latent behavior, and the reduction of false positive and negative fault identification.

Lake eutrophication prediction based on improved MIMO-DD-3Q Learning

“…Traditional Reinforcement Learning [26] Agent interact with the surrounding environment in an unknown environment according to the "Exploration-Utilization" code of conduct, conduct observation and analysis through continuous exploration and discovery, and then continue to learn according to the rewards and punishments obtained, and finally obtain an optimal decision-making process [27]. When traditional reinforcement learning deals with specific learning tasks, the key lies in the establishment of the Agent own state space and action space, as well as the way of interaction with the environment, so as to enable the Agent to find the optimal strategy in the specific learning task.…”