55 citations · 130 across the 6 of their papers we have counts for
6 papers
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.…
A Theoretical Analysis of the Repetition Problem in Text Generation
Zihao Fu, Wai Lam, Anthony Man-Cho So +1
Text generation tasks, including translation, summarization, language models, and etc. see rapid growth during recent years. Despite the remarkable achievements, the repetition pro…
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…
Partially-Aligned Data-to-Text Generation with Distant Supervision
Zihao Fu, Bei Shi, Wai Lam +2
The Data-to-Text task aims to generate human-readable text for describing some given structured data enabling more interpretability. However, the typical generation task is confine…
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…