135 citations · 154 across the 4 of their papers we have counts for
4 papers
Matrix Encoding Networks for Neural Combinatorial Optimization
Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon +3
Machine Learning (ML) can help solve combinatorial optimization (CO) problems better. A popular approach is to use a neural net to compute on the parameters of a given CO problem a…
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim +3
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a gr…
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading
Uk Jo, Taehyun Jo, Wanjun Kim +3
We explore deep Reinforcement Learning(RL) algorithms for scalping trading and knew that there is no appropriate trading gym and agent examples. Thus we propose gym and agent like…
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
Hyungu Kahng, Yonghyun Jeong, Yoon Sang Cho +12
StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level stra…