55 citations · 100 across the 3 of their papers we have counts for
5 papers
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…
Hierarchical Macro Strategy Model for MOBA Game AI
Bin Wu, Qiang Fu, Jing Liang +6
The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an actio…
Sequenced-Replacement Sampling for Deep Learning
Chiu Man Ho, Dae Hoon Park, Wei Yang +1
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-bat…