2 citations · 5 across the 12 of their papers we have counts for
9 papers · 1 filter
T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
Zeyu Shen, Zhuoyuan Wang, Laixi Shi
Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance when nonstationary system dy…
KL-regularization Itself is Differentially Private in Bandits and RLHF
Yizhou Zhang, Kishan Panaganti, Laixi Shi +2
Differential Privacy (DP) provides a rigorous framework for privacy, ensuring the outputs of data-driven algorithms remain statistically indistinguishable across datasets that diff…
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
Shangding Gu, Laixi Shi, Muning Wen +5
Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment s…
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
Chenbei Lu, Laixi Shi, Zaiwei Chen +2
Reinforcement Learning (RL) algorithms are known to suffer from the curse of dimensionality, which refers to the fact that large-scale problems often lead to exponentially high sam…
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
Chengrui Qu, Laixi Shi, Kishan Panaganti +2
Online Reinforcement learning (RL) typically requires high-stakes online interaction data to learn a policy for a target task. This prompts interest in leveraging historical data t…
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
Laixi Shi, Jingchu Gai, Eric Mazumdar +2
Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. To address this, distributionally robust Markov games (RMGs) have been proposed to…
Distributionally Robust Constrained Reinforcement Learning under Strong Duality
Zhengfei Zhang, Kishan Panaganti, Laixi Shi +3
We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal is to maximize the expected reward subject to environmental distribution shifts and constrai…
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
Shangding Gu, Laixi Shi, Yuhao Ding +4
Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. Howeve…
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
Laixi Shi, Eric Mazumdar, Yuejie Chi +1
To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studi…