Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge-Reference-Cited by-同舟云学术

Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge

Published:2023-01-25 Issue: Volume: Page:
ISSN:2633-1403
Container-title:Multi-Agent Technologies and Machine Learning
language:
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Author:

Kravaris Theocharis,A. Vouros George

Abstract

Motivated to solve complex demand-capacity imbalance problems in air traffic management at the pre-tactical stage of operations, with thousands of agents (flights) daily, even in a restricted airspace, in this paper, we review deep multiagent reinforcement learning methods under the prism of their ability to scale toward solving problems with large populations of heterogeneous agents, where agents have to unavoidably decide on their joint policy, without communication constraints.

Publisher

IntechOpen

Link

http://www.intechopen.com/download/pdf/82526

Reference58 articles.

1. Kravaris T, Spatharis C, Bastas A, Vouros GA, Blekas K, Andrienko G, Andrienko N, Garcia JM. Resolving congestions in the air traffic management domain via multiagent reinforcement learning methods. 14 December 2019. arXiv preprint arXiv:1912.06860

2. Sutton RS, Barto AG. Reinforcement learning: An introduction. Cambridge, Massachusetts, USA: MIT Press; 13 November, 2018

3. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, et al. Human-level control through deep reinforcement learning. Nature. 2015;518(7540):529-533

4. Tsitsiklis J, Van Roy B. Analysis of temporal-diffference learning with function approximation. Advances in Neural Information Processing Systems. 1996;9:1075-1081

5. Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N. Dueling network architectures for deep reinforcement learning. In: International Conference on Machine Learning. New York, NY, USA: JMLR: WCP; 11 June 2016. pp. 1995-2003

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