no code implementations • 26 Dec 2023 • Yingkai Xiao, Jingjin Liu, Hankz Hankui Zhuo
Constraint Optimization Problems (COP) pose intricate challenges in combinatorial problems usually addressed through Branch and Bound (B\&B) methods, which involve maintaining priority queues and iteratively selecting branches to search for solutions.
no code implementations • 26 Aug 2023 • Fan Zhang, Kebing Jin, Hankz Hankui Zhuo
Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture implied rules from free-form texts.
no code implementations • 26 Jul 2023 • Weixin Wu, Hankz Hankui Zhuo
Large-scale pre-trained language models such as BERT have contributed significantly to the development of NLP.
no code implementations • 14 Jun 2023 • Xuechen Mu, Hankz Hankui Zhuo, Chen Chen, Kai Zhang, Chao Yu, Jianye Hao
Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
no code implementations • 29 May 2023 • Jingjin Liu, Hankz Hankui Zhuo, Kebing Jin, Jiamin Yuan, Zhimin Yang, Zhengan Yao
Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses.
1 code implementation • 23 May 2023 • Zhanwen Zhou, Hankz Hankui Zhuo, Xiaowu Zhang, Qiyuan Deng
The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license.
no code implementations • 24 Apr 2023 • Chao Yu, Xuejing Zheng, Hankz Hankui Zhuo, Hai Wan, Weilin Luo
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
1 code implementation • 20 Jan 2023 • Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples.
no code implementations • 11 Dec 2022 • Kebing Jin, Yingkai Xiao, Hankz Hankui Zhuo, Renyong Ma
Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices.
no code implementations • 15 Feb 2022 • Kebing Jin, Huaixun Chen, Hankz Hankui Zhuo
Specifically, we first build a novel language model to extract plan traces from texts, and then build a set of constraints to generate action models based on the extracted plan traces.
no code implementations • 15 Feb 2022 • Kebing Jin, Hankz Hankui Zhuo
Natural language processing (NLP) aims at investigating the interactions between agents and humans, processing and analyzing large amounts of natural language data.
no code implementations • 19 Jan 2022 • Jianye Hao, Jiawen Lu, Xijun Li, Xialiang Tong, Xiang Xiang, Mingxuan Yuan, Hankz Hankui Zhuo
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain.
no code implementations • 18 Dec 2021 • Hankz Hankui Zhuo, Shuting Deng, Mu Jin, Zhihao Ma, Kebing Jin, Chen Chen, Chao Yu
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i. e., data efficiency, lack of the interpretability and transferability.
1 code implementation • 11 Dec 2021 • Siqi Hong, Hankz Hankui Zhuo, Kebing Jin, Guang Shao, Zhanwen Zhou
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities.
Ranked #3 on Multi-step retrosynthesis on USPTO-190
no code implementations • 18 Nov 2021 • Xuejing Zheng, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks.
1 code implementation • NeurIPS 2021 • Zifan Wu, Chao Yu, Deheng Ye, Junge Zhang, Haiyin Piao, Hankz Hankui Zhuo
We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting.
no code implementations • 19 Oct 2021 • Kebing Jin, Hankz Hankui Zhuo, Zhanhao Xiao, Hai Wan, Subbarao Kambhampati
In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent.
no code implementations • 15 Mar 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao
To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.
no code implementations • 1 Jan 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Dong Li, Kun Shao, Wulong Liu, Hankz Hankui Zhuo, Jianye Hao
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.
Hierarchical Reinforcement Learning reinforcement-learning +2
no code implementations • 25 Feb 2020 • Huiling Zhu, Xin Luo, Hankz Hankui Zhuo
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications.
no code implementations • 11 Nov 2019 • Junyi Shen, Hankz Hankui Zhuo, Jin Xu, Bin Zhong, Sinno Jialin Pan
However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.
no code implementations • 20 Sep 2019 • Xinghua Zheng, Ming Tang, Hankz Hankui Zhuo, Kevin X. Wen
Bike Sharing Systems (BSSs) have been adopted in many major cities of the world due to traffic congestion and carbon emissions.
no code implementations • 26 Aug 2019 • Hankz Hankui Zhuo, Jing Peng, Subbarao Kambhampati
Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces.
no code implementations • 3 Jun 2019 • Feng Liao, Hankz Hankui Zhuo, Xiaoling Huang, Yu Zhang
Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization.
no code implementations • ICLR 2019 • Xin Luo, Hankz Hankui Zhuo
Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks.
no code implementations • 24 Jan 2019 • Hankz Hankui Zhuo, Wenfeng Feng, Yufeng Lin, Qian Xu, Qiang Yang
In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.
no code implementations • NAACL 2018 • Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo
The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding.
no code implementations • 19 Apr 2018 • Yuncong Li, Hankz Hankui Zhuo
In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years.
no code implementations • 7 Mar 2018 • Wenfeng Feng, Hankz Hankui Zhuo, Subbarao Kambhampati
Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge.
no code implementations • 4 Mar 2018 • Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions.
no code implementations • 20 Mar 2017 • Han Tian, Hankz Hankui Zhuo
Inspired by distributed representations of words in the literature of natural language processing, we propose a novel approach to measuring the similarity of papers based on distributed representations learned from the citation context of papers.
no code implementations • 15 Mar 2017 • Junhua He, Hankz Hankui Zhuo, Jarvan Law
We find that there are often "short" texts describing features of items, based on which we can approximate the similarity of items and make recommendation together with rating scores.
no code implementations • 23 Feb 2017 • Jarvan Law, Hankz Hankui Zhuo, Junhua He, Erhu Rong
Some other models use the information trained from external large corpus to help improving smaller corpus.
no code implementations • 25 Nov 2015 • Yu Zhang, Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, Hankz Hankui Zhuo, Subbarao Kambhampati
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
no code implementations • 18 Nov 2015 • Xin Tian, Hankz Hankui Zhuo, Subbarao Kambhampati
Previous approaches either discover plans by maximally "matching" observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available.