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arXiv Machine Learning By Jiin Woo, Gauri Joshi, Yuejie Chi

Exploration via Exploitation: The Blessing of Reward Diversity in Personalized Federated RL

Read the original on arXiv Machine Learning →

The paper introduces two algorithms for personalized federated reinforcement learning: PF-UCBVI, which achieves optimal linear speedup by separating shared dynamics from individual goals, and PF-EFVI, a greedy method that uses reward diversity to guarantee state-action coverage without explicit exploration bonuses. The authors prove that PF-EFVI attains logarithmic regret under sufficient reward diversity, showing that disagreement among agents can shift the exploration burden from temporal to spatial aspects. This demonstrates that heterogeneous rewards can be leveraged as a structural asset to facilitate efficient collective learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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