Zi-Han Liang

Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost

Fed-LSVI is proposed, the first provably efficient federated algorithm for online reinforcement learning with linear function approximation in episodic Markov decision processes and achieves a regret bound of $\widetilde{\mathcal O}(\sqrt{Md^3H^4T})$, matching the best-known regret for multi-agent online reinforcement...