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The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains.
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Deep Reinforcement Learning framework for Autonomous Driving
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Y. Chen, P. Palanisamy, P. Mudalige, K. Muelling, and J. M. Dolan · 2018
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A. El Sallab, M. Abdou, E. Perot, and S. Yogamani · 2017
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Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
D. Isele, R. Rahimi, A. Cosgun, K. Subramanian, and K. Fujimura · 2017
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RLlib: Abstractions for Distributed Reinforcement Learning
E. Liang, R. Liaw, P. Moritz, R. Nishihara, R. Fox, K. Goldberg, J. E. Gonzalez, M. I. Jordan, and I. Stoica · 2017
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch · 2017
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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
S. Shah, D. Dey, C. Lovett, and A. Kapoor · 2017
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Pomdp and hierarchical options mdp with continuous actions for autonomous driving at intersections
Z. Qiao, K. Muelling, J. Dolan, P. Palanisamy, and P. Mudalige · 2018
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Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles
SAE · 2018
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A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
P. Wang, C.-Y. Chan, and A. de La Fortelle · 2018
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Challenges of Real-World Reinforcement Learning
G. Dulac-Arnold, D. Mankowitz, and T. Hester · 2019
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Wikipedia · 2019
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