PAIR Lab: PKU Alignment and Interaction Research Lab
PAIR Lab: PKU Alignment and Interaction Research Lab
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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
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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
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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
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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
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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
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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
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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
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Online Double Oracle
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial …
Le Cong Dinh
,
Yaodong Yang
,
Stephen McAleer
,
Zheng Tian
,
Nicolas Perez-Nieves
,
Oliver Slumbers
,
David Henry Mguni
,
Haitham Bou Ammar
,
Jun Wang
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Offline Pre-trained Multi-agent Decision Transformer
Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access …
Linghui Meng
,
Muning WEN
,
Chenyang Le
,
Xiyun Li
,
Dengpeng Xing
,
Weinan ZHANG
,
Ying Wen
,
Haifeng ZHANG
,
Jun Wang
,
Yaodong Yang
,
Bo Xu
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Measuring the Non-Transitivity in Chess
In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity …
Ricky Sanjaya
,
Jun Wang
,
Yaodong Yang
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