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Enhanced Rolling Horizon Evolution Algorithm with Opponent Model
  Learning: Results for the Fighting Game AI Competition

Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition

IEEE Transactions on Games (IEEE Trans. Games), 2020
31 March 2020
Zhentao Tang
Yuanheng Zhu
Dongbin Zhao
Simon Lucas
ArXiv (abs)PDFHTML

Papers citing "Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition"

4 / 4 papers shown
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum
  Markov Game
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Guangzheng Hu
Yuanheng Zhu
Haoran Li
Dongbin Zhao
248
8
0
01 Feb 2024
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV
  Air-to-Air Combat
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Jiajun Chai
Wenzhang Chen
Yuanheng Zhu
Zonggui Yao
Dongbin Zhao
BDL
204
63
0
05 Dec 2022
A Deep Reinforcement Learning Blind AI in DareFightingICE
A Deep Reinforcement Learning Blind AI in DareFightingICE
Thai Van Nguyen
Xincheng Dai
Ibrahim Khan
R. Thawonmas
H. V. Pham
VLM
210
12
0
16 May 2022
DareFightingICE Competition: A Fighting Game Sound Design and AI
  Competition
DareFightingICE Competition: A Fighting Game Sound Design and AI Competition
Ibrahim Khan
Thai Van Nguyen
Xincheng Dai
R. Thawonmas
193
21
0
03 Mar 2022
1
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