25 citations · 67 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Learning Multi-agent Action Coordination via Electing First-move Agent
Jingqing Ruan, Linghui Meng, Xuantang Xiong +2
Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously…
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…
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
Linghui Meng, Jin Xu, Xu Tan +3
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking…