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20212024
most citedValue Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

19 citations · 66 across the 13 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.MA2023

ConcaveQ: Non-Monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning

Huiqun Li, Hanhan Zhou, Yifei Zou +2

Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-…

cs.CR2023★ 4 cited

Real-time Network Intrusion Detection via Decision Transformers

Jingdi Chen, Hanhan Zhou, Yongsheng Mei +3

Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detectio…

cs.LG2023★ 11 cited

Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

Hanhan Zhou, Tian Lan, Guru Venkataramani +1

Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due…

cs.LG2023★ 2 cited

Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning

Hanhan Zhou, Tian Lan, Vaneet Aggarwal

Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment. Rece…

cs.NI2023★ 5 cited

Learning-based Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation

Chang-Lin Chen, Hanhan Zhou, Jiayu Chen +8

Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., fe…

cs.LG2023★ 6 cited

MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

Yongsheng Mei, Hanhan Zhou, Tian Lan +2

Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay signific…

cs.LG2023★ 5 cited

ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning

Yongsheng Mei, Hanhan Zhou, Tian Lan

Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution para…