Yudong Luo Department of Decision Science HEC Montreal & Mila - Quebec AI Institute E-mail: yudong [dot] luo [at] hec [dot] ca [Google Scholar]

About

My research interests lie at the intersection of reinforcement learning, risk management, and machine learning.
Currently work as a postdoctoral researcher with Professor Erick Delage.

Publication

* equal contribution, † corresponding

Work in progress

• Measures of Variability for Risk-averse Policy Gradient
  Yudong Luo, Yangchen Pan, Jiaqi Tan, Pascal Poupart
  [arXiv]
• TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
  Sheng Xu, Guiliang Liu, Tarak Kharrat, Yudong Luo et al.
  [arXiv]
• Dynamic risk optimization
  Yudong Luo and Erick Delage
  [arXiv]

Refereed

• RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
  Runyi Zhao, Ruixin Wu, Hongrui Zhang, Chengkun Li, Ang Li, Ruixing Jin, Yueci Deng, Yingying Guo, Lihe Ding, Shaocong Dong, Tianfan Xue, Yanjun Gao, Yudong Luo, Simo Wu, Kui Jia, Wei-Shi Zheng, Guiliang Liu
  Advances in Neural Information Processing Systems (NeurIPS), Competition Track, 2026
  [arXiv]
• Boosting CVaR Policy Optimization with Quantile Gradients
  Yudong Luo and Erick Delage
  International Conference on Machine Learning (ICML), 2026
  [arXiv]
• CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement
  Jiaqi Tan, Yudong Luo, Sophia Huang, Yifan Yang, Hang Ma
  IEEE Robotics and Automation Letters (RA-L), 2026
  [arXiv] [Code] [Video]
• Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities
  Jiaqi Tan*, Yudong Luo*, Jiaoyang Li, Hang Ma
  International Symposium on Combinatorial Search (SoCS), 2025
  Short version accepted to the 2025 RSS Workshop on Scalable and Resilient Multi-Robot Systems
  [arXiv] [Code]
• Risk, Reward, and Reinforcement Learning in Hockey Analytics
  Sheng Xu, Oliver Schulte, Yudong Luo, Pascal Poupart, Guiliang Liu
  Book chapter of Artificial Intelligence and Machine Learning in Sports Science, 2025
  [PDF] [eBook]
• A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization
  Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart
  Reinforcement Learning Conference (RLC), 2024
  [PDF] [Code]
• 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
  [PDF] [Slides] [Code in supplementary]
• Benchmarking Constraint Inference in Inverse Reinforcement Learning
  Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart
  International Conference on Learning Representations (ICLR), 2023
  [PDF] [Code]
• 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
  [PDF] [Slides]
• Distributional Reinforcement Learning with Monotonic Splines
  Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart
  International Conference on Learning Representations (ICLR), 2022
  [PDF] [Poster] [Code in supplementary]
• Learning Selective Communication for Multi-Agent Path Finding
  Ziyuan Ma*, Yudong Luo* †, Jia Pan
  IEEE Robotics and Automation Letters (RA-L), and ICRA, 2022
  [arXiv] [Code] [Demo] [Video]
• Leveraging Approximate Constraints for Localized Data Error Detection
  Mohan Zhang, Oliver Schulte, Yudong Luo
  Intl Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM@SIGMOD), 2021
  [PDF]
• Distributed Heuristic Multi-Agent Path Finding with Communication
  Ziyuan Ma*, Yudong Luo*, Hang Ma
  IEEE International Conference on Robotics and Automation (ICRA), 2021
  [PDF] [Code] [Demo] [Slides]
• Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players
  Yudong Luo, Oliver Schulte, Pascal Poupart
  International Joint Conference on Artificial Intelligence (IJCAI), 2020
  [PDF] [Slides] [Poster] [Code] [Video]
• Valuing Sports Actions and Players with Inverse Reinforcement Learning
  Yudong Luo and Oliver Schulte
  AAAI workshop, Artificial Intelligence in Team Sports (AITS@AAAI), 2020 (spotlight)
  [PDF] [Slides]
• Deep soccer analytics: Learning an action-value function for evaluating soccer players
  Guiliang Liu, Yudong Luo †, Oliver Schulte, Tarak Kharrat
  Data Mining and Knowledge Discovery (DMKD), 2020 (ECML Journal Track)
  [PDF] [Slides]
• EEG-based Emotion Recognition using Domain Adaptation Network
  Yi-Ming Jin, Yu-Dong Luo, Wei-Long Zheng, Bao-Liang Lu
  International Conference on Orange Technologies (ICOT), 2017 (invited paper)
  [PDF] [Slides]

Education

• PhD. Computer Science, University of Waterloo, affiliate with Vector Institute, Canada, Sep. 2020 - Aug. 2024
  → with Professor Pascal Poupart
  Thesis: Policy Learning under Uncertainty and Risk [PDF]
• MSc. Computing Science (Research-based), Simon Fraser University, Canada, Sep. 2018 - Jun. 2020
  → with Professor Oliver Schulte, also work with Professor Hang Ma
  Thesis: Inverse Reinforcement Learning for Team Sports [PDF]
• BEng. Computer Science, Shanghai Jiao Tong University, China, Sep. 2014 - Jun. 2018
  → work with Professor Bao-Liang Lu

Experience

• HEC Montreal, Montreal, Canada. Postdoctoral Researcher. May 2025 - now
• The Chinese University of HongKong (Shenzhen), Shenzhen, China. Visiting Researcher. Jan. 2025 - Apr. 2025
• University of Waterloo, Waterloo, Canada. Visiting Researcher. Sep. 2024 - Dec. 2024
• Noah's Ark Laboratory, Toronto, Canada. Intern. Jul. 2022 - Dec. 2022
• SportLogiq, Vancouver area, Canada. Mitacs Accelerate Program Intern. Mar. 2020 - Jun. 2020
• National University of Singapore, School of Computing. Visiting Researcher. Jul. 2017 - Sep. 2017

Talk

• Measures of Variability for Risk-averse Policy Gradient, Optimization Day, HEC Montreal, May 2025, [Slides]
• Multi-Agent Reinforcement Learning for MAPF, Guest Lecture, Simon Fraser University, Mar. 2025 (online), [Slides]
• An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient, Vector Institute, Nov. 2023 (online), [Slides]
• Mean-Variance Reinforcement Learning, UWaterloo, Dept. of Actuarial Science, Mar. 2023, [Slides]
• Risk-Sensitive Player Evaluation in Sports Game, ETHz, Jan. 2023 (online), [Slides]
• Multi-Agent Path Finding with Deep RL and Communication, USC, Nov. 2022 (online), [Slides]

Note (for self-reference)

• Quantile MDP with Markovian Policy reduces to Quantile-based Distributional RL [PDF] [Web]

Service

• Conference Reviewer: AAMAS 2022; ICRA 2022, 2024, 2025; NeurIPS 2022 - 2026; ICLR 2024, 2025; ICML 2024, 2026; RLC 2024, 2025
• Journal Reviewer: JMLR 2026

Miscellaneous

• Gallery: [Photos]
• Erdős number: [3]

Acknowledgement

My research has been supported by

• the Vector Institute Research (21-24)
• Ontario Graduate Scholarship, awarded by Ontario province [Link] (23)
• President’s Graduate Scholarship, awarded by UWaterloo (23)
• David R. Cheriton Graduate Scholarship, awarded by School of CS, UWaterloo [Link] (23, 24)
• Waterloo AI Institute Graduate Scholarship, awarded by Waterloo.AI [Link] (24)
• Postdoctoral research scholarship, awarded by Fonds de recherche Québec Nature et technologies (FRQNT) [Link] (25-27)
• IVADO Regroupement 9 [Link]

© YD | Last update: Apr. 2026