Ensuring high-density air transportation systems of the future are both safe and efficient is a top priority. With the growing air traffic complexity in traditional and low-altitude airspace, an autonomous air traffic control system is needed to ensure safe-separation requirements. We propose a deep multi-agent reinforcement learning framework that is able to identify and resolve conflicts between aircraft in a high-density, stochastic, and dynamic en route sector with multiple intersections. The proposed framework utilizes an actor-critic model, A2C that incorporates the loss function from Proximal Policy Optimization (PPO) to help stabilize the learning process. In addition, we use a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. We show that our framework is both scalable and efficient for large number of incoming aircraft to achieve extremely high traffic throughput. We evaluate our model via simulation in the BlueSky environment. Results show that our framework is able to resolve 99.97% of all conflicts both along route and at the intersections.
Autonomous Separation Assurance in An High-Density En Route Sector: A Deep Multi-Agent Reinforcement Learning Approach
2019-10-01
1325270 byte
Conference paper
Electronic Resource
English
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