Browse Publications Technical Papers 2004-01-2437

Application of Multi-Agent Reinforcement Learning to RLSS Material Circulation Control System 2004-01-2437

A Regenerative Life Support System (RLSS) is a system that establishes self-sustained material recycling and circulation within a space base on the Moon or Mars. This is a large-scale and complicated system comprising a lot of components such as humans, plants and material circulation system. A RLSS contains many factors with uncertainty, such as dynamics of plants and humans, and failure and performance deterioration of devices. In addition, a RLSS is a large-scale and complicated system extending gradually. An environment with uncertainty or a large-scale and complicated system may not be properly addressed by a centralized system. In particular, such a system cannot always gather accurate information in one center in a frequently shifting environment, thus appropriate processing may be difficult. Therefore, we tried autonomous decentralization of information or decision-making using a Multi-Agent System (MAS). This report discuss the designing of a RLSS material circulation control system using a MAS and a method in which a MAS acquires cooperative action in a bottom-up approach by means of computer simulation. So far, we have confirmed the effectiveness of this method for a RLSS material circulation control system and proved that automatic acquisition of cooperation rules with the autonomous learning among agents are enabled.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 43% off list price.
Login to see discount.
Special Offer: With TechSelect, you decide what SAE Technical Papers you need, when you need them, and how much you want to pay.