This is a pytorch based implementation for our paper on learning to control self-assembling agents using deep reinforcement learning.
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}
}