https://github.com/pathak22/modular-assemblies
[NeurIPS 2019] Code for the paper "Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity"
https://github.com/pathak22/modular-assemblies
artificial-creatures artificial-intelligence assembly compositionality deep-learning modularity morphology reinforcement-learning
Last synced: 2 months ago
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[NeurIPS 2019] Code for the paper "Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity"
- Host: GitHub
- URL: https://github.com/pathak22/modular-assemblies
- Owner: pathak22
- License: other
- Created: 2019-02-15T01:14:15.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-13T22:52:05.000Z (over 5 years ago)
- Last Synced: 2025-03-24T11:13:32.897Z (3 months ago)
- Topics: artificial-creatures, artificial-intelligence, assembly, compositionality, deep-learning, modularity, morphology, reinforcement-learning
- Language: Python
- Homepage: https://pathak22.github.io/modular-assemblies/
- Size: 13.3 MB
- Stars: 116
- Watchers: 6
- Forks: 16
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Learning to Control Self-Assembling Morphologies ##
### NeurIPS 2019 (Spotlight)
Winner of [Virtual Creatures Competition at GECCO 2019, Prague](https://virtualcreatures.github.io/)
#### [[Project Website]](https://pathak22.github.io/modular-assemblies/) [[Demo Video]](https://youtu.be/ngCIB-IWD8E)
[Deepak Pathak](https://people.eecs.berkeley.edu/~pathak/)*, [Chris Lu](https://chris-lu.weebly.com/)*, [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Phillip Isola](https://www.eecs.mit.edu/people/faculty/phillip-isola/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/)
University of California, Berkeley
MIT
(* equal contribution)
This is a pytorch based implementation for our [paper on learning to control self-assembling agents using deep reinforcement learning](https://pathak22.github.io/modular-assemblies/). We investigate a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies. We learn compositional policies to demonstrate better zero-shot generalization. If you find this work useful in your research, please cite:
@inproceedings{pathak19assemblies,
Author = {Pathak, Deepak and Lu, Chris and Darrell, Trevor and
Isola, Phillip and Efros, Alexei A.},
Title = {Learning to Control Self-Assembling Morphologies:
A Study of Generalization via Modularity},
Booktitle = {arXiv preprint arXiv:1902.05546},
Year = {2019}
}
### Installation and Usage
1. Setting up repository
```Shell
git clone https://github.com/pathak22/modular-assemblies.git
cd modular-assemblies/
git clone https://github.com/Unity-Technologies/ml-agents.git
cd ml-agents/
git reset --hard 6c5255e
cd ..
bash envs/setup_env.sh
python3 -m venv assemblyEnv
source $PWD/assemblyEnv/bin/activate
pip install --upgrade pip
```
2. Installation
- Requirements:
- CUDNN-5.1, CUDA-8.0, Python-3.5
- Detailed setup, skip to quick setup for exact replication:
```Shell
# Install Pytorch from http://pytorch.org/
pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp35-cp35m-linux_x86_64.whl
pip install torchvision
pip install --upgrade visdom
# Install baselines for Atari preprocessing
pip install gym==0.9.4 # baselines install latest gym first automatically, but latest gym has moved to mujoco5 so first install old gym and then install baselines
git clone https://github.com/openai/baselines.git
cd baselines
git reset --hard b5be53d
pip install -e .
# Additional packages
pip install numpy
pip install matplotlib
pip install pillow
pip install opencv-python
# fold
cd modular-assemblies/src/
git clone https://github.com/nearai/pytorch-tools.git
cd pytorch-tools/
git reset --hard 09dccb2
python setup.py install
```
- Quick setup for exact replication:
```Shell
pip install -r requirements.txt
```
3. Run code
```Shell
cd modular-assemblies/src/
python test_env.py
```
### Acknowledgement
Builds upon Ilya Kostrikov's Pytorch PPO [implementation](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr).