6 citations · 13 across the 4 of their papers we have counts for
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cs.LG2022★ 5 cited
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Cong Zhang, Zhiguang Cao, Wen Song +2
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far fro…
cs.LG2022★ 6 cited
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Rongkai Zhang, Cong Zhang, Zhiguang Cao +5
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP wi…
cs.LG2022★ 2 cited
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Wenhui Huang, Cong Zhang, Jingda Wu +3
Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach.…