7 citations · 26 across the 6 of their papers we have counts for
6 papers
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.…
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Guangpin Tao, Xiaozhong Ji, Wenzhuo Wang +6
Deep-learning based Super-Resolution (SR) methods have exhibited promising performance under non-blind setting where blur kernel is known. However, blur kernels of Low-Resolution (…
Learning Class-level Prototypes for Few-shot Learning
Minglei Yuan, Wenhai Wang, Tao Wang +3
Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing met…
An Introduction of mini-AlphaStar
Ruo-Ze Liu, Wenhai Wang, Yanjie Shen +3
StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units. Due to its difficulties, such as huge state s…
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…