33 citations · 91 across the 11 of their papers we have counts for
11 papers
Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks
Marc W. Brittain, Luis E. Alvarez, Kara Breeden
Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transpor…
Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction
Shulu Chen, Antony Evans, Marc Brittain +1
Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations wi…
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System
Cooper Cone, Michael Owen, Luis Alvarez +1
The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems i…
AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility
Marc Brittain, Luis E. Alvarez, Kara Breeden +1
We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by…
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…
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance
Wei Guo, Marc Brittain, Peng Wei
The separation assurance task will be extremely challenging for air traffic controllers in a complex and high density airspace environment. Deep reinforcement learning (DRL) was us…
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-…