This repository shows how Deep Reinforcement Learning can be used within Webots. This project was developed under Linux. While it should be possible to use it with other operating systems, this was not tested.
The code presented in this repository was adapted from a semester project at the Distributed Intelligent Systems and Algorithms Laboratory (DISAL). A summary of the project can be found here. The part presented here is about Deep Reinforcement Learning, leveraging Policy Gradient to train a fully connected, feed forward neural network to solve a simple obstacle avoidance task on a continuous action space with a Khepera IV robot.
The project was entirely implemented in C++ and relies on libtorch, PyTorch's C++ API.
Download PyTorch's C++ API. Choose following options:
- Build: Stable
- OS: Linux
- Package: LibTorch
- Language: C++
- CUDA: None
and click on the second link (cxx11 ABI, 87.4M). Extract the zip file wherever you like on your computer. You need to edit the env.sh script and adapt the path to your libtorch installation folder:
export LIBTORCH_PATH=/path/to/libtorch Open up a terminal, go to the project folder and run the following commands to build the project:
source env.sh
./build_project.shPlease visit the repository's wiki for more details.