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Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy.
Asynchronous methods for deep reinforcement learning
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Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D.A.: · 1989
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Reinforcement learning: An introduction. Volume 1
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Introduction to reinforcement learning. Volume 135
Sutton, R.S., Barto, A.G.: · 1998
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Off-road obstacle avoidance through end-to-end learning
Muller, U., Ben, J., Cosatto, E., Flepp, B., Cun, Y.L.: · 2006
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Imitative reinforcement learning for soccer playing robots
Latzke, T., Behnke, S., Bennewitz, M.: · 2006
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An application of reinforcement learning to aerobatic helicopter flight
Abbeel, P., Coates, A., Quigley, M., Ng, A.Y.: · 2007
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Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior
Ziebart, B.D., Maas, A.L., Dey, A.K., Bagnell, J.A.: · 2008
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Learning cpg-based biped locomotion with a policy gradient method: Application to a humanoid robot
Endo, G., Morimoto, J., Matsubara, T., Nakanishi, J., Cheng, G.: · 2008
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Learning from demonstration for autonomous navigation in complex unstructured terrain
Silver, D., Bagnell, J.A., Stentz, A.: · 2010
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: · 2015
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A survey of motion planning and control techniques for self-driving urban vehicles
Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: · 2016
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Is faster r-cnn doing well for pedestrian detection?
Zhang, L., Lin, L., Liang, X., He, K.: · 2016
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Safe, multi-agent, reinforcement learning for autonomous driving
Shalev-Shwartz, S., Shammah, S., Shashua, A.: · 2016
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., et al.: · 2016
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Continuous control with deep reinforcement learning
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: · 2016
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Tree-structured reinforcement learning for sequential object localization
Jie, Z., Liang, X., Feng, J., Jin, X., Lu, W., Yan, S.: · 2016
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Generative adversarial imitation learning
Ho, J., Ermon, S.: · 2016
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Learning to act by predicting the future
Dosovitskiy, A., Koltun, V.: · 2016
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Deep variation-structured reinforcement learning for visual relationship and attribute detection
Liang, X., Lee, L., Xing, E.P.: · 2017
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Attention-aware face hallucination via deep reinforcement learning
Cao, Q., Lin, L., Shi, Y., Liang, X., Li, G.: · 2017
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Recurrent topic-transition gan for visual paragraph generation
Liang, X., Hu, Z., Zhang, H., Gan, C., Xing, E.P.: · 2017
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A.: · 2017
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Virtual to real reinforcement learning for autonomous driving
You, Y., Pan, X., Wang, Z., Lu, C.: · 2017
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Santana, E., Hotz, G.: · 2016
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Deep reinforcement learning framework for autonomous driving
Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: · 2017
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End-to-end learning of driving models from large-scale video datasets
Xu, H., Gao, Y., Yu, F., Darrell, T.: · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
Kim, J., Canny, J.: · 2017
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End-to-end driving via conditional imitation learning
Codevilla, F., Müller, M., Dosovitskiy, A., López, A., Koltun, V.: · 2017
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Fast recurrent fully convolutional networks for direct perception in autonomous driving
Hou, Y., Hornauer, S., Zipser, K.: · 2017
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Carla: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., López, A., Koltun, V.: · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Li, Y., Song, J., Ermon, S.: · 2017
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Learning from demonstrations for real world reinforcement learning
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Sendonaris, A., Dulac-Arnold, G., Osband, I., Agapiou, J., et al.: · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Večerík, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., Riedmiller, M.: · 2017
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Parameter space noise for exploration
Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R.Y., Chen, X., Asfour, T., Abbeel, P., Andrychowicz, M.: · 2017
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D.: · 2017
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Dynamic-structured semantic propagation network
Liang, X., Zhou, H., Xing, E.: · 2018
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Unsupervised real-to-virtual domain unification for end-to-end highway driving
Yang, L., Liang, X., Xing, E.: · 2018
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Reinforcement cutting-agent learning for video object segmentation
Han, J., Yang, L., Zhang, D., Chang, X., Liang, X.: · 2018
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