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FutureTech 應用於自駕模型賽車之深度強化式學習技術
We develop an image-based sim-to-real transfer technique for deep reinforcement learning. First, we train a teacher model to move along a near optimal path. We then use this model to teach a student model the correct actions along with randomization. The technique bridges the sim-to-real gap, improving the driving speedrobustness of the simulator-trained student model in the real world.
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