4 citations · 7 across the 25 of their papers we have counts for
7 papers · 1 filter
Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost
Zihang Liang, Haochen Zhang, Lingzhou Xue
We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, th…
Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study gap-dependent performance guarantees for nearly minimax-optimal algorithms in reinforcement learning with linear function approximation. While prior works have established…
Q-Learning with Fine-Grained Gap-Dependent Regret
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minima…
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-polic…
Gap-Dependent Bounds for Federated -learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We present the first gap-dependent analysis of regret and communication cost for on-policy federated -Learning in tabular episodic finite-horizon Markov decision processes (MDPs…
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
Zhong Zheng, Haochen Zhang, Lingzhou Xue
We study the gap-dependent bounds of two important algorithms for on-policy Q-learning for finite-horizon episodic tabular Markov Decision Processes (MDPs): UCB-Advantage (Zhang et…