This repository contains Rainbow DDPG algorithm from paper Sim-to-Real Reinforcement Learning for Deformable Object Manipulation along with a toy pushing task to demonstrate how to use the code.
The code was tested on Mac OS with Python3.6. Use of virtualenvs is recommended. To run:
pip install -r requirements.txt
python main.py
Runnign a full training may take more than 24 hours on a machine with Nvidia Titan GPU and use a considerable amount of memory.
To run a demonstration of the toy task:
pip install -r requirements.txt
python run_demo.py
Please note that the hyper parameters are not necessarily optimised for the task.
For a complete list of references, please see the accompanying paper.
The learning algorithm is based on OpenAI baselines (https://github.com/openai/baselines), the perlin noise file is heavily based on https://github.com/nikagra/python-noise/blob/master/noise.py and robot meshes are generated from https://github.com/Kinovarobotics/kinova-ros.