Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
Guanren Qiao, Guiliang Liu*, Pascal Poupart, Zhiqiang Xu.
Advances in Neural Information Processing Systems (NeurIPS) 2023.
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient.
Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan.
Advances in Neural Information Processing Systems (NeurIPS) 2023.
Benchmarking Constraint Inference in Inverse Reinforcement Learning.
Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart.
International Conference on Learning Representations (ICLR) 2023.
[Paper (PDF)],
[Slides],
[Code],
Learning Soft Constraints From Constrained Expert Demonstrations.
Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart.
International Conference on Learning Representations (ICLR) 2023.
[Paper (PDF)],
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge.
Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart.
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
[Paper (PDF)],
Year 2022 and Before
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game.
Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart.
Advances in Neural Information Processing Systems (NeurIPS) 2022.
[Paper (PDF)],
[Slides],
[Code (To be released)],
Learning Object-Oriented Dynamics for Planning from Text.
Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart.
International Conference on Learning Representations (ICLR) 2022.
[Paper (PDF)],
[Slides],
[Code],
Distributional Reinforcement Learning with Monotonic Splines.
Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart.
International Conference on Learning Representations (ICLR) 2022.
[Paper (PDF)],
[Code],
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning.
Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart.
Advances in Neural Information Processing Systems (NeurIPS) 2021.
[Paper (PDF)],
[Slides],
[Code],
Learning Agent Representations for Ice Hockey.
Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan.
Advances in Neural Information Processing Systems (NeurIPS) 2020.
[Paper (PDF)],
[Slides],
[Video],
[Code ],
An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction.
Guiliang Liu, Xu Li, Mingming Sun, Ping Li.
SIAM International Conference on Data Mining (SDM) 2020.
[Paper (PDF)]
Extracting Knowledge from Web Text with Monte Carlo Tree Search.
Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li.
The ACM Web Conference (WWW) 2020.
[Paper (PDF)],
[Slides]
Cracking the Black Box: Distilling Deep Sports Analytics.
Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu.
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020.
[Paper (PDF)],
[arXiv],
[Slides],
[ Video],
[Code]
Deep soccer analytics: Learning an action-value function for evaluating soccer players.
Guiliang Liu, Yudong Luo, Oliver Schulte, and Tarak Kharrat.
ECML-PKDD 2020 Journal Track, published at Data Mining and Knowledge Discovery (DMKD).
[Paper (PDF)],
[Slides],
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation.
Guiliang Liu, Oliver Schulte.
International Joint Conference on Artificial Intelligence (IJCAI) 2018.
[Paper (PDF)],
[arXiv],
[Code],
[Slides],
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.
Guiliang Liu, Oliver Schulte, Wang Zhu,
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD) 2018.
[Paper (PDF)],
[arXiv],
[Slides],