Search Results for author:
Found 8 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
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
Built upon such a policy optimization persepctive, our paper extends these subgradient-based search methods to a model-free setting.
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control
We show that the ROA analysis can be approximated as a constrained maximization problem whose goal is to find the worst-case initial condition which shifts the terminal state the most.
Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning
We build a connection between robust adversarial RL and $\mu$ synthesis, and develop a model-free version of the well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with static $D$-scaling.
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
In this paper, we investigate the global convergence of gradient-based policy optimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS).
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
We implement the (data-driven) natural policy gradient method on different MJLS examples.
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning.
Verification and Parameter Synthesis for Stochastic Systems using Optimistic Optimization
As a consequence, we obtain theoretical regret bounds on sample efficiency of our solution that depends on key problem parameters like smoothness, near-optimality dimension, and batch size.
