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Home / Proceedings / Proceedings of the International Conference on Automated Planning and Scheduling, 31 / Book One

A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control

February 1, 2023

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Authors

Supriyo Ghosh

IBM Research AI


Sean Laguna

IBM Research AI


Shiau Hong Lim

IBM Research AI


Laura Wynter

IBM Research AI


Hasan Poonawala

Amazon Web Services


DOI:

10.1609/icaps.v31i1.15993


Abstract:

Reinforcement learning (RL), a promising framework for data-driven decision making in an uncertain environment, has successfully been applied in many real-world operation and control problems. However, the application of RL in a large-scale decentralized multi-agent environment remains a challenging problem due to the partial observability and limited communications between agents. In this paper, we develop a model-based kernel RL approach and a model-free deep RL approach for learning a decentralized, shared policy among homogeneous agents. By leveraging the strengths of both these methods, we further propose a novel deep ensemble multi-agent reinforcement learning (MARL) method that efficiently learns to arbitrate between the decisions of the local kernel-based RL model and the wider-reaching deep RL model. We validate the proposed deep ensemble method on a highly challenging real-world air traffic control problem, where the goal is to provide effective guidance to aircraft to avoid air traffic congestion, conflicting situations, and to improve arrival timeliness, by dynamically recommending adjustments of aircraft speeds in real-time. Extensive empirical results from an open-source air traffic management simulation model, developed by Eurocontrol and built on a real-world data set including thousands of aircrafts, demonstrate that our proposed deep ensemble MARL method significantly outperforms three state-of-the-art benchmark approaches.

Topics: ICAPS

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HOW TO CITE:

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control Proceedings of the International Conference on Automated Planning and Scheduling, 31 (2021) 468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control ICAPS 2021, 468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala (2021). A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control. Proceedings of the International Conference on Automated Planning and Scheduling, 31, 468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control. Proceedings of the International Conference on Automated Planning and Scheduling, 31 2021 p.468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. 2021. A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control. "Proceedings of the International Conference on Automated Planning and Scheduling, 31". 468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. (2021) "A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control", Proceedings of the International Conference on Automated Planning and Scheduling, 31, p.468-476

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala, "A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control", ICAPS, p.468-476, 2021.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. "A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control". Proceedings of the International Conference on Automated Planning and Scheduling, 31, 2021, p.468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. "A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control". Proceedings of the International Conference on Automated Planning and Scheduling, 31, (2021): 468-476.

Supriyo Ghosh||Sean Laguna||Shiau Hong Lim||Laura Wynter||Hasan Poonawala. A Deep Ensemble Method for Multi-Agent Reinforcement Learning: A Case Study on Air Traffic Control. ICAPS[Internet]. 2021[cited 2023]; 468-476.


ISSN: 2334-0843


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Copyright 2022, Association for the Advancement of
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