86 citations · 117 across the 10 of their papers we have counts for
8 papers · 1 filter
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
He Wang, Laixi Shi, Yuejie Chi
In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between t…
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi +1
Offline reinforcement learning (RL), which seeks to learn an optimal policy using offline data, has garnered significant interest due to its potential in critical applications wher…
Offline Reinforcement Learning with On-Policy Q-Function Regularization
Laixi Shi, Robert Dadashi, Yuejie Chi +2
The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history d…
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Wenhao Ding, Laixi Shi, Yuejie Chi +1
Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In t…
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
Laixi Shi, Gen Li, Yuting Wei +3
This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision…
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Laixi Shi, Yuejie Chi
This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history…
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi, Gen Li, Yuting Wei +2
Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage…
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Gen Li, Laixi Shi, Yuxin Chen +1
Achieving sample efficiency in online episodic reinforcement learning (RL) requires optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic Mar…