HROlive / Deep-Reinforcement-Learning-Nanodegree

As one of the top 2% students from the 2nd phase of the "PyTorch Scholarship Challenge" by Facebook AI, I have earned a full scholarship to Udacity’s Deep Reinforcement Learning Nanodegree program

Deep Reinforcement Learning Nanodegree

This repository contains the projects that I've developed during Udacity's Deep Reinforcement Learning Nanodegree program.

Table of Contents

Projects

All of the projects use rich simulation environments from Unity ML-Agents. My solutions to the projects can be found below:

• Navigation: In the first project, I trained an agent to collect yellow bananas while avoiding blue bananas.
• Continuous Control: In the second project, I trained a robotic arm to reach target locations.
• Collaboration and Competition: In the third project, I trained a pair of agents to play tennis!

Resources

• Cheatsheet: You are encouraged to use this PDF file to guide your study of reinforcement learning.

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. 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.

3. 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 .

4. Create an IPython kernel for the drlnd environment.

python -m ipykernel install --user --name drlnd --display-name "drlnd"

5. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.