FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
In the era of increasing privacy concerns and demand for personalized experiences,
traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face …
traditional Reinforcement Learning with Human Feedback (RLHF) frameworks face …
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
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 …
value-based agents operating across diverse Markov Decision Processes (MDPs). Existing …
Reward Poisoning on Federated Reinforcement Learning
E Ma, SR Etesami, P Rathi - Transactions on Machine Learning Research - openreview.net
Federated learning (FL) has become a popular tool for solving traditional Reinforcement
Learning (RL) tasks. The multi-agent structure addresses the major concern of data-hungry …
Learning (RL) tasks. The multi-agent structure addresses the major concern of data-hungry …