Observing the universe with virtual reality satellite is an amazing experience. An intelligent method of attitude control is the core object of research to achieve this goal. Attitude control is essentially one of the goal-state reaching tasks under constraints. Using reinforcement learning methods in real-world systems faces many challenges, such as insufficient samples, exploration safety issues, unknown actuator delays, and noise in the raw sensor data. In this work, a mixed model with different input sizes was proposed to represent the environmental dynamics model. The predication accuracy of the environmental dynamics model and the performance of the policy trained in this paper were gradually improved. Our method reduces the impact of noisy data on the model’s accuracy and improves the sampling efficiency. The experiments showed that the agent trained with our method completed a goal-state reaching task in a real-world system under wireless circumstances whose actuators were reaction wheels, whereas the soft actor-critic method failed in the same training process. The method’s effectiveness is ensured theoretically under given conditions.
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