Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost
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arXiv:2609.00193v1 Announce Type: new Abstract: We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, they typically require sharing raw trajectories. This reliance incurs a c…
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- 2026-09-02 04:00 · arXiv stat.ML
Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost