Search Results for author: Xingang Guo

Found 6 papers, 1 papers with code

Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

no code implementations4 Apr 2024 Darioush Kevian, Usman Syed, Xingang Guo, Aaron Havens, Geir Dullerud, Peter Seiler, Lianhui Qin, Bin Hu

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1. 0 Ultra in solving undergraduate-level control problems.

Model-Free $μ$-Synthesis: A Nonsmooth Optimization Perspective

no code implementations18 Feb 2024 Darioush Keivan, Xingang Guo, Peter Seiler, Geir Dullerud, Bin Hu

Built upon such a policy optimization persepctive, our paper extends these subgradient-based search methods to a model-free setting.

COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability

1 code implementation13 Feb 2024 Xingang Guo, Fangxu Yu, huan zhang, Lianhui Qin, Bin Hu

Based on this connection, we adapt the Energy-based Constrained Decoding with Langevin Dynamics (COLD), a state-of-the-art, highly efficient algorithm in controllable text generation, and introduce the COLD-Attack framework which unifies and automates the search of adversarial LLM attacks under a variety of control requirements such as fluency, stealthiness, sentiment, and left-right-coherence.

Text Generation

Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential

no code implementations20 Oct 2022 Xingang Guo, Bin Hu

In this work, we show that direct policy search is guaranteed to find the global solution of the robust $\mathcal{H}_\infty$ state-feedback control design problem.

Continuous Control

Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation

no code implementations20 Apr 2022 Xingang Guo, Bin Hu

In this paper, we consider the policy evaluation problem in multi-agent reinforcement learning (MARL) and derive exact closed-form formulas for the finite-time mean-squared estimation errors of decentralized temporal difference (TD) learning with linear function approximation.

Multi-agent Reinforcement Learning

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