no code implementations • 28 Apr 2024 • Huanshuo Liu, Bo Chen, Menghui Zhu, Jianghao Lin, Jiarui Qin, Yang Yang, Hao Zhang, Ruiming Tang
Specifically, a knowledge base, consisting of a retrieval-oriented embedding layer and a knowledge encoder, is designed to preserve and imitate the retrieved & aggregated representations in a decomposition-reconstruction paradigm.
no code implementations • 5 Sep 2023 • Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang
To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.
no code implementations • 7 Jun 2023 • Xianyu Chen, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, Menghui Zhu, Ruiming Tang, Kai Dong, Dingyin Xia, Yong Yu
Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.
no code implementations • 18 Dec 2022 • Minghuan Liu, Zhengbang Zhu, Menghui Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions.
no code implementations • 27 Jan 2022 • Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel.
1 code implementation • 20 Jan 2022 • Minghuan Liu, Menghui Zhu, Weinan Zhang
Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios.
1 code implementation • 13 May 2021 • Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
no code implementations • 18 Dec 2020 • Sheng Chen, Menghui Zhu, Deheng Ye, Weinan Zhang, Qiang Fu, Wei Yang
Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome.