10 citations · 11 across the 5 of their papers we have counts for
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cs.LG2024
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
Han Wang, Sihong He, Zhili Zhang +2
We explore a Federated Reinforcement Learning (FRL) problem where agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work…
cs.LG2024★ 1 cited
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
Chenyu Zhang, Han Wang, Aritra Mitra +1
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing the sample complexity of reinforcement learning tasks by exploiting information from differe…
cs.LG2023★ 10 cited
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Guangchen Lan, Han Wang, James Anderson +2
Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains…