27 citations · 27 across the 3 of their papers we have counts for
6 papers
Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers
Sydney M. Katz, Luis E. Alvarez, Michael Owen +4
Aircraft collision avoidance systems have long been a key factor in keeping our airspace safe. Over the past decade, the FAA has supported the development of a new family of collis…
Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling
Andrew Weinert, Marc Brittain, Ngaire Underhill +1
As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned…
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance
Marc Brittain, Xuxi Yang, Peng Wei
A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynam…
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Marc Brittain, Peng Wei
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controll…
Prioritized Sequence Experience Replay
Marc Brittain, Josh Bertram, Xuxi Yang +1
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more effici…
Hierarchical Reinforcement Learning with Deep Nested Agents
Marc Brittain, Peng Wei
Deep hierarchical reinforcement learning has gained a lot of attention in recent years due to its ability to produce state-of-the-art results in challenging environments where non-…