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Exploration via Exploitation: The Blessing of Reward Diversity in Personalized Federated RL
Not provided in the abstract
federated learningreinforcement learningpersonalization
2611.12964
Builder Relevance
8h ago70%
Abstract
This paper presents a novel approach to federated reinforcement learning that utilizes reward diversity as a means to enhance exploration and collective learning.
Reality Card
Core Claim
The introduction of Personalized Federated Exploration-Free Value Iteration (PF-EFVI) achieves logarithmic regret without explicit exploration bonuses by leveraging reward diversity.
Method / Result
PF-EFVI attains logarithmic regret under sufficient reward diversity.
Limitations
The paper does not specify the authors or provide detailed experimental results, which may limit reproducibility.
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