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We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions.
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R. S. Sutton, J. Modayil, M. Delp, T. Degris, P. M. Pilarski, A. White, and D. Precup, “Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction,” in The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 , ser. AAMAS ’11. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2011, pp. 761–768. [Online]. Available: http://dl.acm.org/citation.cfm?id=2031678.2031726
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P. M. Pilarski, M. R. Dawson, T. Degris, F. Fahimi, J. P. Carey, and R. S. Sutton, “Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning,” in 2011 IEEE International Conference on Rehabilitation Robotics , June 2011, pp. 1–7
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J. Modayil, A. White, and R. S. Sutton, “Multi-timescale nexting in a reinforcement learning robot,” in From Animals to Animats 12 , T. Ziemke, C. Balkenius, and J. Hallam, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 299–309
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N. T. S. Board, “The use of forward collision avoidance systems to prevent and mitigate rear-end crashes,” National Transportation Safety Board, Tech. Rep. NTSB/SIR-15/01, 2015
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J. Günther, P. M. Pilarski, G. Helfrich, H. Shen, and K. Diepold, “Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning,” Mechatronics , vol. 34, pp. 1 – 11, 2016, system-Integrated Intelligence: New Challenges for Product and Production Engineering. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957415815001555
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2018
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