Controllability-Constrained-Latent-Models

Setup

1. clone the repo:

git clone https://github.com/suruchi1997/ControlledVAE.git

2. setup the anaconda environment:
   cuda: conda env create --file env_cuda.yml
   cpu: conda env create --file env_cpu.yml

Reproducing Results

Regenerating model results for each experiment is divided into a few steps:

1. Training M baseline models. Since the baseline model may be sensitive to weight initialization, we provide a --rs option to set the random seed. A few good seeds are available in rseeds.txt.
2. Training N models within a range of beta values based on each baseline model. Beta values have to be consistent across all seeds. We provide a range of beta values in betas.txt.
3. Evaluating all trained models.
4. Making plots.

Experiments

Both Pendulum and Cartpole follow the similar steps for training and evaluating the models:

1. train.py The base models and controllability-constrained models are trained and saved in the models/ folder.

• (option A) Slower, in sequence training:
  use python train.py to train all models in sequence. Will use rseeds.txt to train M base models, and betas.txt to train N models for each rseed.
• (option B) Parallelized training:
  1. Train M base models + beta=0 in parallel python train.py --rs {integer} --beta 0.0
     Slurm script for training all base models: sbatch train_parallel_base.sh
  2. Train N models for each remaining beta value, using python train.py --rs {integer} --beta {float}
     Slurm script for training the rest of the models: sbatch train_parallel_ctrl.sh
• (option C) Run B.ii in parallel for all rseed-beta combinations if the overhead of re-learning the base model is not a concern.

2. Then python eval.py is used to execute the evaluation process. Same options as for training.
   slurm script for evaluation: sbatch eval_parallel.sh

Making plots

1. cd <experiment>
2. python ../csv_com.py - generate csv files based on evaluation results.
3. python ../plots.py - generate the "Control Cost Change in %" vs "Degree of Controllability" plot.

GPU's used

For Pendulum

NVIDIA P-100 GPUs

For CartPole

NVIDIA A100-PCIE-40GB - to speed up the training process for complex system

Link to Datasets

https://drive.google.com/drive/u/0/folders/1piegH2gSNOG6WZtlOgSI3MLtDAUxj7ng

Above is the link to datasets in order to regenerate the results in paper. The model can be data set oriented and has a lot of scope for improvement