Search Results for author: Xiangyuan Zhang

Found 7 papers, 2 papers with code

Policy Optimization for PDE Control with a Warm Start

no code implementations1 Mar 2024 Xiangyuan Zhang, Saviz Mowlavi, Mouhacine Benosman, Tamer Başar

The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction.

Dimensionality Reduction

Controlgym: Large-Scale Safety-Critical Control Environments for Benchmarking Reinforcement Learning Algorithms

1 code implementation30 Nov 2023 Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi, Mouhacine Benosman, Tamer Başar

This project serves the learning for dynamics & control (L4DC) community, aiming to explore key questions: the convergence of RL algorithms in learning control policies; the stability and robustness issues of learning-based controllers; and the scalability of RL algorithms to high- and potentially infinite-dimensional systems.

Benchmarking OpenAI Gym +1

Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs

1 code implementation9 Sep 2023 Xiangyuan Zhang, Saviz Mowlavi, Mouhacine Benosman, Tamer Başar

We introduce the receding-horizon policy gradient (RHPG) algorithm, the first PG algorithm with provable global convergence in learning the optimal linear estimator designs, i. e., the Kalman filter (KF).

Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient

no code implementations25 Feb 2023 Xiangyuan Zhang, Tamer Başar

We revisit in this paper the discrete-time linear quadratic regulator (LQR) problem from the perspective of receding-horizon policy gradient (RHPG), a newly developed model-free learning framework for control applications.

Learning the Kalman Filter with Fine-Grained Sample Complexity

no code implementations30 Jan 2023 Xiangyuan Zhang, Bin Hu, Tamer Başar

We develop the first end-to-end sample complexity of model-free policy gradient (PG) methods in discrete-time infinite-horizon Kalman filtering.

Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

no code implementations NeurIPS 2021 Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Başar

Direct policy search serves as one of the workhorses in modern reinforcement learning (RL), and its applications in continuous control tasks have recently attracted increasing attention.

Continuous Control Multi-agent Reinforcement Learning +2

Non-Cooperative Inverse Reinforcement Learning

no code implementations NeurIPS 2019 Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Başar

Through interacting with the more informed player, the less informed player attempts to both infer, and act according to, the true objective function.

reinforcement-learning Reinforcement Learning (RL)

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