stanfordasl / barc

Contains the code for "BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning" by Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone.

BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

This repository contains the code for BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning by Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, and Marco Pavone

Programming Environment

git clone https://github.com/StanfordASL/BaRC.git
cd BaRC             # or wherever you cloned the repo

# openai/baselines requires Python 3.5, so we enforce it too.
conda create --name backreach python=3.5
source activate backreach
pip install numpy matplotlib scipy

Dependencies

This repository depends on OpenAI Gym, OpenAI Baselines, and OpenMPI (because of Baselines). Further, our code requires MATLAB as well as the helperOC and Level Set Methods toolboxes for backward reachability computations.

For a minimal installation, you can install OpenAI Gym and Baselines like so:

cd gym
pip install -e '.[mujoco,atari,classic_control,robotics]'

cd ../code/baselines
pip install -e .

With those obtained, you must place our two gym environments DrivingOrigin-v0 and PlanarQuad-v0 located in the gym/ folder into your gym installation. Instructions for how to do this can be found on the OpenAI gym website.

Then, place our modifications to the baselines PPO algorithm into your installation of OpenAI baselines. They are located under code/baselines/. For this, you simply replace the ppo1 folder in the baselines repository with ours.

Now you're ready to use our code. train.py is the main runner interface which can be called with command line arguments.