In this article, we explore the computation of joint policies for autonomous agents, representing flights, to resolve congestions problems in the Air Traffic Management (ATM) domain in the context of Demand-Capacity Balance (DCB) process. We formalize the problem as a multi-agent Markov Decision Process (MDP) towards deciding flight ground delays to resolve imbalances, during the pre-tactical phase. To this end, we present and evaluate multi-agent reinforcement learning methods. An experimental study on real-world cases confirms the effectiveness of our approach.
Multiagent Reinforcement Learning Methods for Resolving Demand - Capacity Imbalances
2018-09-01
1528804 byte
Conference paper
Electronic Resource
English
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