7 citations · 18 across the 4 of their papers we have counts for
4 papers · 1 filter
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
Ruo-Ze Liu, Zhen-Jia Pang, Zhou-Yu Meng +3
StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon.…
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao +8
Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collecte…
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
Ruo-Ze Liu, Haifeng Guo, Xiaozhong Ji +5
Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This paper presents our discovery that an abstrac…
On Reinforcement Learning for Full-length Game of StarCraft
Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng +3
StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investig…