2 citations · 7 across the 14 of their papers we have counts for
8 papers · 1 filter
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
Wenzheng Jiang, Ji Wang, Xiongtao Zhang +3
Federated Reinforcement Learning (FedRL) improves sample efficiency while preserving privacy; however, most existing studies assume homogeneous agents, limiting its applicability i…
FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
Flint Xiaofeng Fan, Cheston Tan, Yew-Soon Ong +2
In the era of increasing privacy concerns and demand for personalized experiences, traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face significant challen…
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
Zhenglin Wan, Xingrui Yu, David Mark Bossens +5
Imitation learning (IL) has shown promise in various applications (e.g. robot locomotion) but is often limited to learning a single expert policy, constraining behavior diversity a…
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
Hei Yi Mak, Flint Xiaofeng Fan, Luca A. Lanzendörfer +3
In this study, we delve into Federated Reinforcement Learning (FedRL) in the context of value-based agents operating across diverse Markov Decision Processes (MDPs). Existing FedRL…
Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence
Philip Jordan, Florian Grötschla, Flint Xiaofeng Fan +1
In Federated Reinforcement Learning (FRL), agents aim to collaboratively learn a common task, while each agent is acting in its local environment without exchanging raw trajectorie…
FedHQL: Federated Heterogeneous Q-Learning
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw…
Federated Neural Bandits
Zhongxiang Dai, Yao Shu, Arun Verma +3
Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward…
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-…