Using reinforcement learning to control advanced life support systems

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.