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Deep Reinforcement Learning Nanodegree

Trained Agents

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program.

Table of Contents

Tutorials

The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

Labs / Projects

The labs and projects can be found below. All of the projects use rich simulation environments from Unity ML-Agents. In the Deep Reinforcement Learning Nanodegree program, you will receive a review of your project. These reviews are meant to give you personalized feedback and to tell you what can be improved in your code.

Resources

OpenAI Gym Benchmarks

Classic Control

Box2d

Toy Text

Dependencies

To set up your python environment to run the code in this repository, follow the instructions below.

  1. Create (and activate) a new environment with Python 3.6.

    • Linux or Mac:
    conda create --name drlnd python=3.6
    source activate drlnd
    
    • Windows:
    conda create --name drlnd python=3.6 
    activate drlnd
    
  2. If running in Windows, ensure you have the "Build Tools for Visual Studio 2019" installed from this site. This article may also be very helpful. This was confirmed to work in Windows 10 Home.

  3. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

    • Next, install the classic control environment group by following the instructions here.
    • Then, install the box2d environment group by following the instructions here.
  4. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

    git clone https://github.com/udacity/deep-reinforcement-learning.git
    cd deep-reinforcement-learning/python
    pip install .
    
  5. Create an IPython kernel for the drlnd environment.

    python -m ipykernel install --user --name drlnd --display-name "drlnd"
    
  6. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

Kernel

Want to learn more?

<p align="center">Come learn with us in the <a href="https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893">Deep Reinforcement Learning Nanodegree</a> program at Udacity!</p> <p align="center"><a href="https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893"> <img width="503" height="133" src="https://user-images.githubusercontent.com/10624937/42135812-1829637e-7d16-11e8-9aa1-88056f23f51e.png"></a> </p>