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Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Tan, M · 1993
Earlier work this paper cites.
Automated conflict resolution for air traffic management using cooperative multiagent negotiation
Wollkind, S., Valasek, J., and Ioerger, T · 2004
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Automated conflict resolution for air traffic control
Erzberger, H · 2005
Earlier work this paper cites.
Fast-time simulation evaluation of a conflict resolution algorithm under high air traffic demand
Farley, T. and Erzberger, H · 2007
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Bu, L., Babu, R., De Schutter, B., et al · 2008
Earlier work this paper cites.
High-level reinforcement learning in strategy games
Amato, C. and Shani, G · 2010
Earlier work this paper cites.
Algorithm and operational concept for resolving short-range conflicts
Erzberger, H. and Heere, K · 2010
Earlier work this paper cites.
Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
Matignon, L., Laurent, G. J., and Le Fort-Piat, N · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Autonomy research for civil aviation: toward a new era of flight
Council, N. R. et al · 2014
Earlier work this paper cites.
Design principles and algorithms for air traffic arrival scheduling
Erzberger, H. and Itoh, E · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Revising the airspace model for the safe integration of small unmanned aircraft systems
Air, A. P · 2015
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Google uas airspace system overview, 2015
Google · 2015
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Safely enabling civilian unmanned aerial system (uas) operations in low-altitude airspace by unmanned aerial system traffic management (utm)
Kopardekar, P. H · 2015
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Air traffic management technology demonstration-1 concept of operations (atd-1 conops), version 3.0
Baxley, B. T., Johnson, W. C., Scardina, J., and Shay, R. F · 2016
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Using a deep reinforcement learning agent for traffic signal control
Genders, W. and Razavi, S · 2016
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Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
Chen, Y. F., Liu, M., Everett, M., and How, J. P · 2017
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Enabling airspace integration for high-density on-demand mobility operations
Mueller, E. R., Kopardekar, P. H., and Goodrich, K. H · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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U-space blueprint
Undertaking, S. J · 2017
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Starcraft ii: A new challenge for reinforcement learning
Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., Schrittwieser, J., et al · 2017
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Hoekstra, J. M. and Ellerbroek, J · 2016
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Fast-forwarding to a future of on-demand urban air transportation
Holden, J. and Goel, N · 2016
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Unmanned aircraft system traffic management (utm) concept of operations
Kopardekar, P., Rios, J., Prevot, T., Johnson, M., Jung, J., and Robinson, J. E · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Alphago: Mastering the ancient game of go with machine learning
Silver, D. and Hassabis, D · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Blueprint for the sky, 2018
Airbus · 2018
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Autonomous aircraft sequencing and separation with hierarchical deep reinforcement learning
Brittain, M. and Wei, P · 2018
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Motion planning among dynamic, decision-making agents with deep reinforcement learning
Everett, M., Chen, Y. F., and How, J. P · 2018
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Deep reinforcement learning for traffic light control in vehicular networks
Liang, X., Du, X., Wang, G., and Han, Z · 2018
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Uber elevate - the future of urban air transport, 2018
Uber · 2018
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Service-oriented separation assurance for small uas traffic management
Hunter, G. and Wei, P · 2019
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