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Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.
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Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, p. 12, 2019
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W. Yuan, K. Hang, D. Kragic, M. Y. Wang, and J. A. Stork, “End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer,” Robotics and Autonomous Systems , vol. 119, pp. 119–134, 2019
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A. Barreto, W. Dabney, R. Munos, J. J. Hunt, T. Schaul, H. P. van Hasselt, and D. Silver, “Successor features for transfer in reinforcement learning,” in Advances in neural information processing systems , 2017, pp. 4055–4065
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2019
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C. Nadiger, A. Kumar, and S. Abdelhak, “Federated reinforcement learning for fast personalization,” in 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) . IEEE, 2019, pp. 123–127
2019
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