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Framework for relational reinforcement learning implemented in PyTorch.

We include additional features, beyond RLKit, aimed at supporting high step complexity tasks and relational RL:

  • Training with multiple parallel workers via MPI
  • Concise implementations of inductive graph neural net architectures
  • Padded and masked minibatching for simultaneous training over variable-sized observation graphs
  • Observation graph input module for block construction task
  • Tuned example scripts for block construction task

Implemented algorithms:

To get started, checkout the example scripts, linked above.

If you find this code useful, please cite:

  Author = {Li, Richard and
  Jabri, Allan and Darrell, Trevor and Agrawal, Pulkit},
  Title = {Towards Practical Multi-object Manipulation using Relational Reinforcement Learning},
  Booktitle = {ICRA},
  Year = {2020}


Note: These settings have only been tested on Ubuntu 18. It is recommended to use Ubuntu 18.

  1. Install and activate a new python3.6+ virtualenv. (3.6+ is only needed because f-strings are used liberally in the code.. you can change the f-strings to support lower versions of python).
virtualenv -p python3 relationalrl_venv
source relationalrl_venv/bin/activate

For the following steps, make sure you are sourced inside the relationalrl_venv virtualenv.

  1. Install numpy.
pip install numpy
  1. Prepare for mujoco-py installation.

    1. Download mjpro150
    2. cd ~
    3. mkdir .mujoco
    4. Move mjpro150 folder to .mujoco
    5. Move mujoco license key mjkey.txt to ~/.mujoco/mjkey.txt

    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro150/bin

    1. For Ubuntu, run:

    sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3

    sudo apt install -y patchelf

  2. Install supporting packages

pip install -r requirements.txt

Make sure pip is with python3!!

  1. Install Fetch Block Construction environment:
git clone
cd fetch-block-construction
pip install -e .
  1. Copy to and fill out with desired config settings:
cp rlkit/launchers/ rlkit/launchers/

    export PYTHONPATH=$PYTHONPATH:<path/to/rlkit-relational>

  2. Add the export statements above to .bashrc to avoid needing to run them everytime you login.

  3. Optional: to save videos with the policy visualization script, install ffmpeg:

    sudo apt-get install ffmpeg

Running scripts

Make sure to set mode in the scripts:

  • here_no_doodad: run locally, without Docker
  • local_docker: locally with Docker
  • ec2: Amazon EC2

To run multiple workers under the here_no_doodad setting, run the following command in the command line:

mpirun -np <numworkers> python examples/relationalrl/

Using a GPU

You can use a GPU by setting mode="gpu_opt" in the example scripts.

Visualizing a policy and seeing results

During training, the results will be saved to a file called under

  • LOCAL_LOG_DIR is the directory set by rlkit.launchers.config.LOCAL_LOG_DIR. Default name is 'output'.
  • <exp_prefix> is given either to setup_logger.
  • <foldername> is auto-generated and based off of exp_prefix.
  • inside this folder, you should see a file called params.pkl. To visualize a policy, run
(rlkit) $ python scripts/ LOCAL_LOG_DIR/<exp_prefix>/<foldername>/params.pkl

To visualize results, download viskit. You can visualize results with:

python viskit/viskit/ LOCAL_LOG_DIR/<exp_prefix>/

This viskit repo also has a few extra nice features, like plotting multiple Y-axis values at once, figure-splitting on multiple keys, and being able to filter hyperparametrs out.

Launching jobs with doodad

The run_experiment function makes it easy to run Python code on Amazon Web Services (AWS) or Google Cloud Platform (GCP) by using doodad.

It's as easy as:

from rlkit.launchers.launcher_util import run_experiment

def function_to_run(variant):
    learning_rate = variant['learning_rate']

    mode='ec2',  # or 'gcp'
    variant={'learning_rate': 1e-3},

You will need to set up parameters in (see step one of Installation). This requires some knowledge of AWS and/or GCP, which is beyond the scope of this README. To learn more, more about doodad, go to the repository.


Much of the coding infrastructure and base algorithm implementations are courtesy of RLKit.

The Dockerfile is based on the OpenAI mujoco-py Dockerfile.


Codebase for ICRA 2020 paper "Towards Practical Multi-object Manipulation using Relational Reinforcement Learning"







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