1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning
Yue Jin, Shuangqing Wei, Jian Yuan +1
Learning to coordinate is a daunting problem in multi-agent reinforcement learning (MARL). Previous works have explored it from many facets, including cognition between agents, cre…
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…
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Yongchao Liu, Yue Jin, Yong Chen +4
Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions t…