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In this paper, we discuss the results of applying the ARIC and GARIC architectures, which have been developed for reinforcement learning using fuzzy logic, to the attitude control of the Space Shuttle. This paper demonstrates that it is possible to control the pitch, roll, and yaw of the Space Shuttle within a specified deadband by using fuzzy control rules and automatically adapt to a reduced error tolerance. The performance of this controller is compared with a controller using conventional control theory and also a non-adaptive fuzzy controller. Our results, using the Orbital Operations Simulator (00s) system, demonstrate that more difficult tasks can be learned by our controller while the fuel efficiency remains very high.