25 citations · 90 across the 15 of their papers we have counts for
7 papers · 1 filter
Large Sequence Models for Sequential Decision-Making: A Survey
Muning Wen, Runji Lin, Hanjing Wang +6
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer visio…
An Empirical Study on Google Research Football Multi-agent Scenarios
Yan Song, He Jiang, Zheng Tian +6
Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open…
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…
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
Xue Yan, Yali Du, Binxin Ru +3
The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluati…
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
Linghui Meng, Muning Wen, Yaodong Yang +7
Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also d…
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
Chenguang Wang, Yaodong Yang, Oliver Slumbers +4
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-bas…
Settling the Variance of Multi-Agent Policy Gradients
Jakub Grudzien Kuba, Muning Wen, Yaodong Yang +5
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL…