Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management-Reference-Cited by-同舟云学术

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management

Published:2022-06-06 Issue:4 Volume:53 Page:4063-4098
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Kravaris Theocharis^ORCID,Lentzos Konstantinos,Santipantakis Georgios,Vouros George A.,Andrienko Gennady,Andrienko Natalia,Crook Ian,Garcia Jose Manuel Cordero,Martinez Enrique Iglesias

Funder

SESAR Joint Undertaking under European Union Horizon 2020 research and innovation programme

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03605-1.pdf

Reference69 articles.

1. Agogino AK, Tumer K (2012) A multiagent approach to managing air traffic flow. Auton Agents Multiagent Syst 24:1–25

2. Bazzan ALC (2009) Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Auton Agent Multi-Agent Syst 18:342–375

3. Kuyer L, Whiteson S, Bakker B, Vlassis N (2008) Multiagent reinforcement learning for urban traffic control using coordination graphs. Mach Learn Knowl Discov Database:656–671

4. Tumer K, Agogino A (2007) Distributed agent-based air traffic flow management. International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’07)

5. Walraven E, Spaan MTJ, B.Bakker (2016) Traffic flow optimization: A reinforcement learning approach. Eng Appl Artif Intell 52:203–212

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