Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Yaodong Yang

Yaodong Yang

A Game-Theoretic Approach to Multi-Agent Trust Region Optimization


Jun 12, 2021
Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

Figure 1 for A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Figure 2 for A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Figure 3 for A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Figure 4 for A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
* A Multi-Agent Trust Region Learning (MATRL) algorithm that augments the single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game 
  Access Paper or Ask Questions

Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games


Jun 10, 2021
Xiangyu Liu, Hangtian Jia, Ying Wen, Yaodong Yang, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu

Figure 1 for Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
Figure 2 for Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
Figure 3 for Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
Figure 4 for Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
* We investigate a new perspective on unifying diversity measures for open-ended learning in zero-sum games, which shapes an auto-curriculum to induce diverse yet effective behaviors 
  Access Paper or Ask Questions

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning


Jun 05, 2021
Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang

Figure 1 for MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Figure 2 for MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Figure 3 for MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Figure 4 for MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
* 24 pages, 17 figures, 5 tables 
  Access Paper or Ask Questions

Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games


Jun 04, 2021
Xidong Feng, Oliver Slumbers, Yaodong Yang, Ziyu Wan, Bo Liu, Stephen McAleer, Ying Wen, Jun Wang

Figure 1 for Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games
Figure 2 for Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games
Figure 3 for Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games
Figure 4 for Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games
* corresponding to  
  Access Paper or Ask Questions

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment


Jun 03, 2021
Tianze Zhou, Fubiao Zhang, Kun Shao, Kai Li, Wenhan Huang, Jun Luo, Weixun Wang, Yaodong Yang, Hangyu Mao, Bin Wang, Dong Li, Wulong Liu, Jianye Hao

Figure 1 for Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Figure 2 for Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Figure 3 for Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Figure 4 for Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
* 12 pages, 9 figures 
  Access Paper or Ask Questions

Learning to Shape Rewards using a Game of Switching Controls


Mar 16, 2021
David Mguni, Jianhong Wang, Taher Jafferjee, Nicolas Perez-Nieves, Wenbin Song, Yaodong Yang, Feifei Tong, Hui Chen, Jiangcheng Zhu, Yali Du, Jun Wang

Figure 1 for Learning to Shape Rewards using a Game of Switching Controls
Figure 2 for Learning to Shape Rewards using a Game of Switching Controls
Figure 3 for Learning to Shape Rewards using a Game of Switching Controls
Figure 4 for Learning to Shape Rewards using a Game of Switching Controls

  Access Paper or Ask Questions

Online Double Oracle


Mar 16, 2021
Le Cong Dinh, Yaodong Yang, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou Ammar, Jun Wang

Figure 1 for Online Double Oracle
Figure 2 for Online Double Oracle
Figure 3 for Online Double Oracle
Figure 4 for Online Double Oracle
* [email protected] 
  Access Paper or Ask Questions

Modelling Behavioural Diversity for Learning in Open-Ended Games


Mar 14, 2021
Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers, David Henry Mguni, Jun Wang

Figure 1 for Modelling Behavioural Diversity for Learning in Open-Ended Games
Figure 2 for Modelling Behavioural Diversity for Learning in Open-Ended Games
Figure 3 for Modelling Behavioural Diversity for Learning in Open-Ended Games
Figure 4 for Modelling Behavioural Diversity for Learning in Open-Ended Games
* corresponds to  
  Access Paper or Ask Questions

Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction


Mar 03, 2021
Hongyao Tang, Jianye Hao, Guangyong Chen, Pengfei Chen, Chen Chen, Yaodong Yang, Luo Zhang, Wulong Liu, Zhaopeng Meng

Figure 1 for Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Figure 2 for Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Figure 3 for Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Figure 4 for Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
* Accepted paper on AAAI 2021. arXiv admin note: text overlap with arXiv:1905.11100 
  Access Paper or Ask Questions

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems


Feb 16, 2021
Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou Ammar, Jun Wang, Matthew E. Taylor

* AAMAS 2021 
  Access Paper or Ask Questions

An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective


Nov 01, 2020
Yaodong Yang, Jun Wang

Figure 1 for An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
Figure 2 for An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
Figure 3 for An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
Figure 4 for An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective

  Access Paper or Ask Questions

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving


Nov 01, 2020
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang

Figure 1 for SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Figure 2 for SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Figure 3 for SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Figure 4 for SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
* 20 pages, 11 figures. Paper accepted to CoRL 2020 
  Access Paper or Ask Questions

What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator


Oct 19, 2020
Hongyao Tang, Zhaopeng Meng, Jianye HAO, Chen Chen, Daniel Graves, Dong Li, Wulong Liu, Yaodong Yang

Figure 1 for What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
Figure 2 for What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
Figure 3 for What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
Figure 4 for What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
* Preprint version 
  Access Paper or Ask Questions

Learning to Infer User Hidden States for Online Sequential Advertising


Sep 03, 2020
Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Weinan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

Figure 1 for Learning to Infer User Hidden States for Online Sequential Advertising
Figure 2 for Learning to Infer User Hidden States for Online Sequential Advertising
Figure 3 for Learning to Infer User Hidden States for Online Sequential Advertising
Figure 4 for Learning to Infer User Hidden States for Online Sequential Advertising
* to be published in CIKM 2020 
  Access Paper or Ask Questions

Multi-Agent Determinantal Q-Learning


Jun 09, 2020
Yaodong Yang, Ying Wen, Liheng Chen, Jun Wang, Kun Shao, David Mguni, Weinan Zhang

Figure 1 for Multi-Agent Determinantal Q-Learning
Figure 2 for Multi-Agent Determinantal Q-Learning
Figure 3 for Multi-Agent Determinantal Q-Learning
Figure 4 for Multi-Agent Determinantal Q-Learning
* ICML 2020 
  Access Paper or Ask Questions

$α^α$-Rank: Scalable Multi-agent Evaluation through Evolution


Sep 28, 2019
Yaodong Yang, Rasul Tutunov, Phu Sakulwongtana, Haitham Bou Ammar, Jun Wang

Figure 1 for $α^α$-Rank: Scalable Multi-agent Evaluation through Evolution
Figure 2 for $α^α$-Rank: Scalable Multi-agent Evaluation through Evolution
Figure 3 for $α^α$-Rank: Scalable Multi-agent Evaluation through Evolution
Figure 4 for $α^α$-Rank: Scalable Multi-agent Evaluation through Evolution
* The authors decide to retract the current version for an improvement 
  Access Paper or Ask Questions

Spectral-based Graph Convolutional Network for Directed Graphs


Jul 21, 2019
Yi Ma, Jianye Hao, Yaodong Yang, Han Li, Junqi Jin, Guangyong Chen

Figure 1 for Spectral-based Graph Convolutional Network for Directed Graphs
Figure 2 for Spectral-based Graph Convolutional Network for Directed Graphs
Figure 3 for Spectral-based Graph Convolutional Network for Directed Graphs
Figure 4 for Spectral-based Graph Convolutional Network for Directed Graphs

  Access Paper or Ask Questions

Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets


Jun 11, 2019
Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

Figure 1 for Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Figure 2 for Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Figure 3 for Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Figure 4 for Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets

  Access Paper or Ask Questions

Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction


May 27, 2019
Hongyao Tang, Jianye Hao, Guangyong Chen, Pengfei Chen, Zhaopeng Meng, Yaodong Yang, Li Wang

Figure 1 for Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Figure 2 for Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Figure 3 for Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Figure 4 for Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
* 10 pages for paper and 6 pages for the supplementary material 
  Access Paper or Ask Questions

Multi-Agent Generalized Recursive Reasoning


Jan 26, 2019
Ying Wen, Yaodong Yang, Rui Lu, Jun Wang

Figure 1 for Multi-Agent Generalized Recursive Reasoning
Figure 2 for Multi-Agent Generalized Recursive Reasoning
Figure 3 for Multi-Agent Generalized Recursive Reasoning
Figure 4 for Multi-Agent Generalized Recursive Reasoning

  Access Paper or Ask Questions

Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning


Jan 26, 2019
Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan

Figure 1 for Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Figure 2 for Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Figure 3 for Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Figure 4 for Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
* ICLR 2019 
  Access Paper or Ask Questions