1 citations · 3 across the 14 of their papers we have counts for
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Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
Yiting He, Zhishuai Liu, Weixin Wang +1
Off-dynamics reinforcement learning (RL), where training and deployment transition dynamics are different, can be formulated as learning in a robust Markov decision process (RMDP)…
Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation
Jingwen Gu, Yiting He, Zhishuai Liu +1
Decision-making under distribution shift is a central challenge in reinforcement learning (RL), where training and deployment environments differ. We study this problem through the…
A Differential Manifold Perspective and Universality Analysis of Continuous Attractors in Artificial Neural Networks
Shaoxin Tian, Hongkai Liu, Yuying Yang +5
Continuous attractors are critical for information processing in both biological and artificial neural systems, with implications for spatial navigation, memory, and deep learning…
How to Provably Improve Return Conditioned Supervised Learning?
Zhishuai Liu, Yu Yang, Ruhan Wang +2
In sequential decision-making problems, Return-Conditioned Supervised Learning (RCSL) has gained increasing recognition for its simplicity and stability in modern decision-making t…
Linear Mixture Distributionally Robust Markov Decision Processes
Zhishuai Liu, Pan Xu
Many real-world decision-making problems face the off-dynamics challenge: the agent learns a policy in a source domain and deploys it in a target domain with different state transi…
Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
Cheng Tang, Zhishuai Liu, Pan Xu
The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value func…
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
Ruhan Wang, Yu Yang, Zhishuai Liu +2
We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. O…
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning
Zhishuai Liu, Weixin Wang, Pan Xu
We study off-dynamics Reinforcement Learning (RL), where the policy training and deployment environments are different. To deal with this environmental perturbation, we focus on le…
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
Zhishuai Liu, Pan Xu
Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for functio…
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
Zhishuai Liu, Pan Xu
We study off-dynamics Reinforcement Learning (RL), where the policy is trained on a source domain and deployed to a distinct target domain. We aim to solve this problem via online…
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
Zhen Qin, Zhishuai Liu, Pan Xu
signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients. Several standard analyses assume independent stochastic-gr…