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Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffic, and the decision-making in complex multi-agent settings.
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Igor Mordatch and Pieter Abbeel · 2017
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The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
Philip Polack, Florent Altché, Brigitte d’Andréa Novel, and Arnaud de La Fortelle · 2017
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor · 2017
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Flow: Architecture and benchmarking for reinforcement learning in traffic control
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, and Alexandre M Bayen · 2017
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Intrinsic social motivation via causal influence in multi-agent rl. corr abs/1810.08647 (2018)
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A Ortega, DJ Strouse, Joel Z Leibo, and Nando de Freitas · 2018
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Edouard Leurent · 2018
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John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2018
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Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, and Alexandre M Bayen · 2018
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Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
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Learning to walk in the real world with minimal human effort, 2020
Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, and Jie Tan · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Curriculum learning for reinforcement learning domains: A framework and survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E Taylor, and Peter Stone · 2020
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Emergent road rules in multi-agent driving environments
Avik Pal, Jonah Philion, Yuan-Hong Liao, and Sanja Fidler · 2020
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Multi-agent connected autonomous driving using deep reinforcement learning
Praveen Palanisamy · 2020
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Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, et al · 2019
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2019
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2019
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Towards standardization of av safety: C++ library for responsibility sensitive safety
Bernd Gassmann, Fabian Oboril, Cornelius Buerkle, Shuang Liu, Shoumeng Yan, Maria Soledad Elli, Ignacio Alvarez, Naveen Aerrabotu, Suhel Jaber, Peter van Beek, Darshan Iyer, and Jack Weast · 2019
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Learning a decision module by imitating driver’s control behaviors, 2019
Junning Huang, Sirui Xie, Jiankai Sun, Qiurui Ma, Chunxiao Liu, Jianping Shi, Dahua Lin, and Bolei Zhou · 2019
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Learning to drive in a day
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, and Amar Shah · 2019
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Waymo open dataset: An autonomous driving dataset, 2019
Waymo LLC · 2019
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Is independent learning all you need in the starcraft multi-agent challenge?
Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip HS Torr, Mingfei Sun, and Shimon Whiteson · 2020
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Responsive safety in reinforcement learning by pid lagrangian methods
Adam Stooke, Joshua Achiam, and Pieter Abbeel · 2020
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Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving, 2020
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Drivergym: Democratising reinforcement learning for autonomous driving
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