Deep reinforcement learning-based air combat maneuver decision-making: literature review, implementation tutorial and future direction
Top Cited Papers
- 28 December 2023
- journal article
- review article
- Published by Springer Nature in Artificial Intelligence Review
- Vol. 57 (1) , 1-39
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (12102077, 12161076, U2241263)
- Fundamental Research Funds for the Central Universities (DUT22RC(3)010, DUT22LAB305, DUT22ZD211)
This publication has 134 references indexed in Scilit:
- Backward Q-learning: The combination of Sarsa algorithm and Q-learningEngineering Applications of Artificial Intelligence, 2013
- Reinforcement learning in robotics: A surveyThe International Journal of Robotics Research, 2013
- Analysis and improvement of policy gradient estimationNeural Networks, 2012
- Air Combat Situation Assessment by Gray Fuzzy Bayesian NetworkApplied Mechanics and Materials, 2011
- Hessian matrix distribution for Bayesian policy gradient reinforcement learningInformation Sciences, 2011
- A modified ant optimization algorithm for path planning of UCAVApplied Soft Computing, 2008
- Sequential maneuvering decisions based on multi-stage influence diagram in air combatJournal of Systems Engineering and Electronics, 2007
- Game-theoretic modeling and control of a military air operationIEEE Transactions on Aerospace and Electronic Systems, 2001
- Amalgamating knowledge basesACM Transactions on Database Systems, 1994
- Game theory for automated maneuvering during air-to-air combatJournal of Guidance, Control, and Dynamics, 1990