55 citations · 99 across the 4 of their papers we have counts for
5 papers
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Lanxiao Huang, Tyler Cody, Christopher Redino +8
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detect…
Learning Diverse Policies in MOBA Games via Macro-Goals
Yiming Gao, Bei Shi, Xueying Du +10
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Ev…
Towards Playing Full MOBA Games with Deep Reinforcement Learning
Deheng Ye, Guibin Chen, Wen Zhang +15
MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc.…
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Deheng Ye, Guibin Chen, Peilin Zhao +15
We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA…
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
Deheng Ye, Zhao Liu, Mingfei Sun +15
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and…