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    Using reinforcement learning to control advanced life support systems

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    Author
    Klein, Theresa J.
    Date
    2005
    Advisor
    Subramanian, Devika
    Degree
    Master of Science
    Abstract
    This thesis deals with the application of reinforcement learning techniques to the control of a closed life support system simulator, such as could be used on a long duration space mission. We apply reinforcement learning to two different aspects of the simulator, control of recycling subsystems, and control of crop planting schedules. Comparisons are made between distributed and centralized controllers, generalized and non-generalized RL, and between different approaches to the construction of the state table and the design of reward functions. Distributed controllers prove to be superior to centralized controllers both in terms of speed and performance of the controller. Generalization helps to speed convergence, but the performance of the policy derived is dependent on the shape of the reward function.
    Keyword
    Aerospace engineering; Electronics; Electrical engineering; Computer science
    Citation
    Klein, Theresa J.. "Using reinforcement learning to control advanced life support systems." (2005) Master’s Thesis, Rice University. https://hdl.handle.net/1911/17794.
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    • ECE Theses and Dissertations [597]
    • Rice University Electronic Theses and Dissertations [13783]

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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
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    Site Map