9 citations · 26 across the 5 of their papers we have counts for
5 papers
Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
Xiangchun Chen, Jiannong Cao, Zhixuan Liang +2
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fu…
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Fei Ni, Jianye Hao, Yao Mu +4
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL). However, these works mostly lack the generalizati…
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Zhixuan Liang, Yao Mu, Mingyu Ding +3
Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the…
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation
Zhixuan Liang, Jiannong Cao, Shan Jiang +2
Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep re…
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning
Zhiuxan Liang, Jiannong Cao, Shan Jiang +3
Multi-agent reinforcement learning (MARL) has been gaining extensive attention from academia and industries in the past few decades. One of the fundamental problems in MARL is how…