jainraj / cisr_neurips20

Curriculum Induction for Safe Reinforcement learning (CISR)

Overview

This repository contains the implementation of the Curriculum Induction for Safe Reinforcement learning (CISR) framework and the code to reproduce the experiments presented in the paper "Safe Reinforcement Learning via Curriculum Induction", Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause and Alekh Agarwal.

Installation

We recommend Ubuntu 18+ and Python 3.7+ installation using Anaconda.

To install, run:

git clone https://github.com/zuzuba/SafeCL.git
cd SafeCL
./install.sh

This script creates a conda environment and automatically installs all the dependencies necessary to reproduce the experiments in the paper. If you have OpenAI gym environment working already then just run,

pip install -e .

Running Experiments

For each environment considered in the paper (lunar lander and frozen lake), we provide pre-trained teachers and the data for their evaluation with 10 independent students. Therefore, for each of the environments, it is possible to :

• Plot: Plot or print the table of the comparison of a curriculum induced by a teaching policy optimized with CISR against the baselines. The plot corresponds to figure 3 of the paper and is only available for frozen lake. The values in the tables correspond to tables 1, 6, 7a, 7b and 7c.
• Evaluate: Run the comparison of a teacher that was pre-trained with CISR against against the baselines.
• Train: Train a new teacher from scratch using CISR (~1 hour for frozen lake and 5-7 hours for lunar lander).

For both environments, we provide a compare_teachers.py script, which performs the plot and evaluate functions and a teacher_learning.py script, which carries out the train function. All the results are stored in the ./results directory.

Frozen lake

Plot

To generate the plots run the compare_teachers script with the --plot flag. You can use the --teacher_dir flag to specify which pre-trained teachers you want to use among those saved in ./results/flake/teacher_training. For example, the following command plots the comparison for the default teacher:

python src/teacher/flake_approx/compare_teachers.py --plot

The following command plots the comparison for all the available teachers

python src/teacher/flake_approx/compare_teachers.py --plot --teacher_dir 03_06_20__11_46_57 03_06_20__12_20_36 02_06_20__11_46_57

Evaluate

To compare the trained teacher against the baselines, it is sufficient to run the compare_teachers.py script with the --evaluate flag. Similar to the plotting case, the --teacher_dir flag can be used to specify the teacher to run the comparison for.

Train

To train a new teacher, you need to run the teacher_learning.py script:

python src/teacher/flake_approx/teacher_learning.py

The trained teacher as well as some information about the training process will be stored in ./results/flake/teacher_training in a directory named after the date and time when the training process is performed. If you want to evaluate and/or plot this newly trained teacher, it is sufficient to pass the name of this directory as an argument to the compare_teachers.py script (see above).

Lunar Lander

Analyze

To generate the tables with the statistics relative to the lunar lander experiments run the compare_teachers script with the --analyze flag. In this case, a positional argument that specifies the scenario is required:

• Scenario 0: Two-layered teacher with noiseless observation (Table 7a)
• Scenario 1: One-layered teacher with noiseless observation (Table 7c)
• Scenario 2: Two-layered teacher with noisy observation (Table 7b)

You can use the --teacher_dir flag to specify which pre-trained teachers you want to use among those saved in ./results/lunar_lander/teacher_training. For example, the following command prints the table with the comparison for the default teacher for the two-layered student, noiseless scenario:

python src/teacher/lunar_lander/compare_teachers.py 0

The following command plots the comparison for all the available teachers for the one-layered student, noiseless scenario

python src/teacher/lunar_lander/compare_teachers.py 1 --analyze --teacher_dir 03_06_20__18_24_43 01_06_20__16_10_17 09_06_20__19_21_22

Evaluate

To compare the trained teacher against the baselines, it is sufficient to run the compare_teachers.py script with the --evaluate flag. Similar to the plotting case, a scenario must be specified and the --teacher_dir flag can be used to specify the teacher to run the comparison for.

Train

To train a new teacher, you need to run the teacher_learning.py script:

python src/teacher/lunar_lander/teacher_learning.py

The trained teacher as well as some information about the training process will be stored in ./results/flake/teacher_training in a directory named after the date and time when the training process is performed. If you want to evaluate and/or get the evaluation statistics table of this newly trained teacher, it is sufficient to pass the name of this directory as an argument to the compare_teachers.py script (see above).

Citation

Please refer to paper Safe Reinforcement Learning via Curriculum Induction) for further details. Please cite this as:

@inproceedings{cisr2020neurips,
  title={Safe Reinforcement Learning via Curriculum Induction},
  author={Matteo Turchetta and Andrey Kolobov and Shital Shah and Andreas Krause and Alekh Agarwal},
  year={2020},
  eprint={2006.12136},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url = {https://arxiv.org/abs/1705.05065}
}

License

This project is released under the MIT License. Please review the License file for more details.