Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin
Differentiable programming in Taichi allows you to optimize neural network controllers efficiently with brute-force gradient descent, instead of using reinforcement learning.
- Create a conda virtual environment and activate it.
conda create -n diffsim python=3.6 -y
conda activate diffsim
- Download and build the project.
git clone git@github.com:YilingQiao/diffsim.git
cd diffsim
bash script_build.sh
cd pysim
- Run the examples
python exp_learn_cloth.py
python exp_learn_stick.py
@aritical{Qiao2020Scalable,
author = {Qiao, Yiling and Liang, Junbang and Koltun, Vladlen and Lin, Ming C.},
title = {Scalable Differentiable Physics for Learning and Control},
booktitle = {ICML},
year = {2020},
}