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Reinforcement Learning in the Control of a Simulated Life Support System
Technical Paper
2004-01-2440
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
To make extended space missions, such as missions to Mars, a reality, an advanced life support system (ALS) must be developed that is able to utilize resources to their fullest capabilities [2]. In order to make such a system a reality, a robust control system must be developed that is able to cope with the complexity of an ALS.
This work applies reinforcement learning (RL), a machine learning technique, to the task of controlling the water recovery system of a simulated ALS. The RL agent learns an effective control strategy that extends the mission length to the point that lack of water is no longer the cause of mission termination.
Authors
Citation
Quasny, T. and Pyeatt, L., "Reinforcement Learning in the Control of a Simulated Life Support System," SAE Technical Paper 2004-01-2440, 2004, https://doi.org/10.4271/2004-01-2440.Also In
References
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