Reinforcement Learning in the Control of a Simulated Life Support System
2004-01-2440
07/19/2004
- Event
- Content
- 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.
- Pages
- 9
- 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.