Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning

no code implementations • 14 Mar 2024 • Zhishuai Liu, Pan Xu

Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces.

Offline RL Reinforcement Learning (RL)

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation

1 code implementation • 23 Feb 2024 • Zhishuai Liu, Pan Xu

We provide the first study on online DRMDPs with function approximation for off-dynamics RL.

Reinforcement Learning (RL)

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization

no code implementations • 24 Oct 2023 • Zhen Qin, Zhishuai Liu, Pan Xu

Yet, existing analyses of signSGD rely on assuming that data are sampled with replacement in each iteration, contradicting the practical implementation where data are randomly reshuffled and sequentially fed into the algorithm.