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Heterogeneous-Agent Reinforcement Learning

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI …

Yifan Zhong, Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang

ASP: Learn a Universal Neural Solver!

Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy. However, …

Chenguang Wang, Zhouliang Yu, Stephen McAleer, Tianshu Yu, Yaodong Yang

Safe Multi-agent Reinforcement Learning for Multi-robot Control

A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, …

Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, Yaodong Yang

Is Nash Equilibrium Approximator Learnable?

In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated …

Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng

Large Sequence Models for Sequential Decision-Making: A Survey

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in …

Muning Wen, Runji Lin, HanjingWANG, Yaodong Yang, Ying Wen, Luo Mai, Jun Wang, Haifeng Zhang, Weinan Zhang

A Deep Reinforcement Learning-driven Vine Copula Method for Correlation Structure Analysis of Mortgage

Controlling risk is the key to playing a core role in financial services and effectively serving the high-quality development of the …

Qinghao Wang, Yanling PENG, Yijie Peng, Yaodong Yang

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

Population-based multi-agent reinforcement learning (PB-MARL) encompasses a range of methods that merge dynamic population selection …

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

On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games

We introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state stochastic games repeated in the infinite horizon and prove its PPAD-completeness.

Xiaotie Deng, Ningyuan Li, David Mguni, Jun Wang, Yaodong Yang

Solving Inventory Management Problems through Deep Reinforcement Learning

Inventory management (e.g. lost sales) is a central problem in supply chain management. Lost sales inventory systems with lead times …

Qinghao Wang, Yijie Peng, Yaodong Yang

MSRL: Distributed Reinforcement Learning with Dataflow Fragments

Reinforcement learning (RL) trains many agents, which is resource-intensive and must scale to large GPU clusters. Different RL training …

Huanzhou Zhu, Bo Zhao, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen