Deep Reinforcement Learning in Competing Cognitive Tactical Networks

Authors

  • Yi Chen

DOI:

https://doi.org/10.54097/hset.v32i.5186

Keywords:

cognitive tactical network, multi-agent reinforcement learning, deep reinforcement learning, Actor-Critic method.

Abstract

These instructions give you guidelines for preparing papers for DRP. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at DRP. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd-Fe-B"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field, but are not required. Put a space between authors’ initials. The abstract must be a concise yet comprehensive reflection of what is in your article. In particular, the abstract must be self-contained, without abbreviations, footnotes, or references. It should be a microcosm of the full article. The abstract must be between 100 - 300 words. Be sure that you adhere to these limits; otherwise, you will need to edit your abstract accordingly. The abstract must be written as one paragraph, and should not contain displayed mathematical equations or tabular material. The abstract should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.

Downloads

Download data is not yet available.

References

Wang, B., & Liu, K. R., "Advances in cognitive radio networks: A survey," IEEE Journal of selected topics in signal processing, 5(1), 5-23(2010).

Zhang, K., Yang, Z., & Başar, T., "Multi-agent reinforcement learning: A selective overview of theories and algorithms," Handbook of Reinforcement Learning and Control, 321-384(2021).

Abramson, N., "THE ALOHA SYSTEM: Another Alternative for Computer Communications," Proceedings of the ACM Fall Joint Computer Conference, 281-285(1970).

Dastangoo, S., Fossa, C. and Gwon, Y., "Competing Cognitive Tactical Networks," Lincoln Laboratory Journal, 16-35(2014).

Gwon, Y., Dastangoo, S., and Kung, H.T., "Competing Mobile Network Game: Embracing Antijamming and Jamming Strategies with Reinforcement Learning," IEEE Conference on Communications and Network Security, 2013.

Watkins, C. J., & Dayan, P., "Q-learning," Machine learning, 8(3), 279-292(1992).

Bellman, R., "Dynamic Programming," Princeton University Press, 1957.

Hu, J., & Wellman, M. P., "Nash Q-learning for general-sum stochastic games," Journal of machine learning research, 4(Nov), 1039-1069(2003).

Littman, M. L., "Friend-or-foe Q-learning in general-sum games," International Conference on Machine Learning , 322-328(2001).

Carta, S., Ferreira, A., Podda, A. S., Recupero, D. R., & Sanna, A., "Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting," Expert systems with applications, 164, 113820(2021).

Konda, V. and Tsitsikls, J., "Actor-Critic Algorithms," Advances in Neural Information Processing Systems 12(1999).

Downloads

Published

12-02-2023