This is the official implementation of the paper Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs authored by X. Zhang, S. Mowlavi, M. Benosman, and T. Başar.
3D visualizations of different filters in the estimation of the convection-diffusion equation.
2D visualizations of different filters compared with the ground truth.
To run the experiments described in the paper, execute
cd LearningKF
python setup_pde.py
python train_kf.py
To reproduce the plots presented in the paper, run
cd LearningKF/tools
# Plot eigen spectrum of the convection-diffusion model
python 1_plot_eigen.py
# Evolve the ground truth PDE trajectory
python 2_save_plot_pde_traj.py
# Visualize the trajectory estimated by model-based KF
python 3_test_plot_KF_traj.py
# Visualize trajectories estimated by RHPG filters with different horizon length N
python 4_test_plot_rhpg_traj.py
# Test the performances of different filters across different initial conditions
python 5_test_save_rand_init.py
python 6_plot_rand_init.py
The sturcture of the repository is as follows.
LearningKF
├── setup_pde.py
├── train_kf.py
├── utils
├── helper.py
└── mb_control.py
└── tools
├── 0_check_obsv.py
├── 1_plot_eigen.py
├── 2_save_plot_pde_traj.py
├── 3_test_plot_KF_traj.py
├── 4_test_plot_rhpg_traj.py
├── 5_test_save_rand_init.py
└── 6_plot_rand_init.py
If you use the software, please cite the following arXiv preprint:
@article{zhang2023global,
title = {Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs},
author = {Zhang, Xiangyuan and Mowlavi, Saviz and Benosman, Mouhacine and Ba{\c{s}}ar, Tamer},
journal = {arXiv preprint arXiv:2309.04831},
year = {2023}
}