Search Results for author:
Found 7 papers, 6 papers with code
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
In this work, we investigate a set of RL techniques for the full-length game of StarCraft II.
Rethinking of AlphaStar
After the discussion, we present the future research directions for these problems.
An Introduction of mini-AlphaStar
StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units.
Novelty-Prepared Few-Shot Classification
We disclose that a classically fully trained feature extractor can leave little embedding space for unseen classes, which keeps the model from well-fitting the new classes.
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
Injecting human knowledge is an effective way to accelerate reinforcement learning (RL).
On Reinforcement Learning for Full-length Game of StarCraft
The reinforcement training algorithm for this architecture is also investigated.
Hierarchical Reinforcement Learning
reinforcement-learning
+4
Shape Robust Text Detection with Progressive Scale Expansion Network
To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
Ranked #12 on
Scene Text Detection
on ICDAR 2017 MLT
