Reinforcement Learning with Unity and Pytorch
This repository contains several intuitive, high quality implementations of state-of-the-art reinforcement learning algorithms solving Unity-based environments.
Table of Contents
This directory contains everything you need to train and evaluate an Agent that learns to collect bananas in a large environment.
This directory contains everything you need to train and evaluate an Agent that learns to control a robotic arm.
This directory contains everything you need to train and evaluate two Agents that learn to play tennis in a Multi-Agent-Scenario.
Each subproject is designed as a stand-alone project and can be used without the others.
All environments were built in Unity. For more information on any of the subproject, please refer to the README.md
inside the subproject's directory.
Getting started
To set up the necessary dependencies, follow the steps described here.
(
Disclaimer
: This steps assume that you are using Anaconda. If you don't, I highly recommend you to do so.
You also might want to use Jupyter Notebooks for a more visual experience during training / evaluation.)
Linux or Mac:
conda create --name rlup python=3.6
source activate rlup
Windows:
conda create --name rlup python=3.6
activate rlup
Clone the repository (if you haven't already!), and cd into the setup folder. Then, install several dependencies.
git clone https://github.com/HFBN/Reinforcement-Learning-with-Unity-and-PyTorch.git
cd deep-reinforcement-learning/python
pip install .
There have been some issues with Windows and pytorch as well as TensorFlow. Therefore, I recommend to install them
manually.
conda install -c conda-forge tensorflow==1.14.0
conda install -c pytorch pytorch==1.4.0
Create an IPython kernel for the rlup environment.
python -m ipykernel install --user --name rlup --display-name "rlup"
Before running code in a notebook, change the kernel to match the rlup environment by using the drop-down Kernel menu.
AuthorJonas J. Mühlbauer, Artifical Intelligence Consultant
Intuitive, high-quality Implementations of several State-of-the-Art Reinforcement Learning Algorithms solving various Unity-based Environments.
Topics
reinforcement-learning
deep-learning
unity
deep-reinforcement-learning
q-learning
pytorch
deep-q-network
deep-q-learning
multi-agent-reinforcement-learning