1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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…