313 citations · 1.8k across the 83 of their papers we have counts for
20 papers · 1 filter
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
Chuming Li, Jie Liu, Yinmin Zhang +5
Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policie…
Contextual Transformer for Offline Meta Reinforcement Learning
Runji Lin, Ye Li, Xidong Feng +6
The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is…
TorchOpt: An Efficient Library for Differentiable Optimization
Jie Ren, Xidong Feng, Bo Liu +4
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution patterns, and their execution needs massive…
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Huanzhou Zhu, Bo Zhao, Gang Chen +6
Reinforcement learning (RL) trains many agents, which is resource-intensive and must scale to large GPU clusters. Different RL training algorithms offer different opportunities for…
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao +6
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible d…
GenDexGrasp: Generalizable Dexterous Grasping
Puhao Li, Tengyu Liu, Yuyang Li +4
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior a…
End-to-End Affordance Learning for Robotic Manipulation
Yiran Geng, Boshi An, Haoran Geng +3
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can…
Constrained Update Projection Approach to Safe Policy Optimization
Long Yang, Jiaming Ji, Juntao Dai +5
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a…
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Yali Du, Chengdong Ma, Yuchen Liu +4
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks. Such a challenge is more outstanding in m…
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation
Zhitao Zhu, Shijing Si, Jianzong Wang +2
Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to pr…
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding +3
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research communit…
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang +8
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through re…
On the Convergence of Fictitious Play: A Decomposition Approach
Yurong Chen, Xiaotie Deng, Chenchen Li +4
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in -player games, which builds the foundation for modern…
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer +3
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game t…
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Muning Wen, Jakub Grudzien Kuba, Runji Lin +4
Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning…
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
Shangding Gu, Long Yang, Yali Du +4
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world application…
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
Xiaotian Hao, Hangyu Mao, Weixun Wang +5
The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample eff…
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Zehao Dou, Jakub Grudzien Kuba, Yaodong Yang
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games. For such a decomposition rule to hol…
Settling the Communication Complexity for Distributed Offline Reinforcement Learning
Juliusz Krysztof Ziomek, Jun Wang, Yaodong Yang
We study a novel setting in offline reinforcement learning (RL) where a number of distributed machines jointly cooperate to solve the problem but only one single round of communica…
Efficient Policy Space Response Oracles
Ming Zhou, Jingxiao Chen, Ying Wen +4
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation ine…