6 papers
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…
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…
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…
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…
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…