7 citations · 9 across the 16 of their papers we have counts for
16 papers
The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation
Cuijie Xu, Yuanfan Xu, Min Xue +5
While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like ro…
Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games
Bingyu Hui, Lebin Yu, Quanming Yao +3
Zero-shot coordination(ZSC), a key challenge in multi-agent game theory, has become a hot topic in reinforcement learning (RL) research recently, especially in complex evolving gam…
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Lebin Yu, Yunbo Qiu, Quanming Yao +3
Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is…
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
Hengyu Zhang, Zhilin Lu, Xudong Zhang +1
In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) ba…
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help
Yunbo Qiu, Yue Jin, Lebin Yu +2
Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years. However, in the local reward scheme, where only local rewards for each a…
Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems
Zhilin Lu, Xudong Zhang, Rui Zeng +1
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Generalized spatial modulation (GSM) i…
Improving Zero-Shot Coordination Performance Based on Policy Similarity
Lebin Yu, Yunbo Qiu, Quanming Yao +2
Over these years, multi-agent reinforcement learning has achieved remarkable performance in multi-agent planning and scheduling tasks. It typically follows the self-play setting, w…
Low Entropy Communication in Multi-Agent Reinforcement Learning
Lebin Yu, Yunbo Qiu, Qiexiang Wang +2
Communication in multi-agent reinforcement learning has been drawing attention recently for its significant role in cooperation. However, multi-agent systems may suffer from limita…
Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems
Zhilin Lu, Xudong Zhang, Rui Zeng +1
Channel state information (CSI) feedback is necessary for the frequency division duplexing (FDD) multiple input multiple output (MIMO) systems due to the channel non-reciprocity. W…
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
Xudong Zhang, Zhilin Lu, Rui Zeng +1
In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following…
Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback
Zhilin Lu, Xudong Zhang, Rui Zeng +1
The channel state information (CSI) needs to be fed back from the user equipment (UE) to the base station (BS) in frequency division duplexing (FDD) multiple-input multiple-output…
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
Yunbo Qiu, Yuzhu Zhan, Yue Jin +2
Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the coo…
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Yunbo Qiu, Yue Jin, Jian Wang +1
Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions wi…
Point Cloud Change Detection With Stereo V-SLAM:Dataset, Metrics and Baseline
Zihan Lin, Jincheng Yu, Lipu Zhou +3
Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing…
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning
Yue Jin, Shuangqing Wei, Jian Yuan +1
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorith…
Robust Reinforcement Learning under model misspecification
Lebin Yu, Jian Wang, Xudong Zhang
Reinforcement learning has achieved remarkable performance in a wide range of tasks these days. Nevertheless, some unsolved problems limit its applications in real-world control. O…