Toward Conflict Resolution with Deep Multi-Agent Reinforcement Learning

What is it about?

Safety in Air Traffic Management at the tactical level is ensured by human controllers. Automatic Detection and Resolution tools are one way to assist controllers in their tasks. In this paper, we develop a model multi-agent reinforcement learning model that not only solves the conflicts but takes into account other factors such as sustainability and the environmental impact by considering fuel consumption and delays.

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