https://github.com/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
https://github.com/hrolive/deep-reinforcement-learning-nanodegree

control deep-learning deep-reinforcement-learning navigation python reinforcement-learning

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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

• Host: GitHub
• URL: https://github.com/hrolive/deep-reinforcement-learning-nanodegree
• Owner: HROlive
• Created: 2019-08-14T22:37:50.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-27T01:06:28.000Z (over 6 years ago)
• Last Synced: 2025-01-04T16:23:59.105Z (over 1 year ago)
• Topics: control, deep-learning, deep-reinforcement-learning, navigation, python, reinforcement-learning
• Language: Jupyter Notebook
• Homepage: https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893
• Size: 4.29 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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[//]: # (Image References)

[image1]: https://user-images.githubusercontent.com/10624937/42135602-b0335606-7d12-11e8-8689-dd1cf9fa11a9.gif "Trained Agents"

[image2]: https://user-images.githubusercontent.com/10624937/42386929-76f671f0-8106-11e8-9376-f17da2ae852e.png "Kernel"

# Deep Reinforcement Learning Nanodegree

![Trained Agents][image1]

This repository contains the projects that I've developed during Udacity's [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.  

## Table of Contents

### Projects

All of the projects use rich simulation environments from [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents). My solutions to the projects can be found below:

* [Navigation](https://github.com/HROlive/Deep-Reinforcement-Learning-Nanodegree/tree/master/project_1-navigation): In the first project, I trained an agent to collect yellow bananas while avoiding blue bananas.

* [Continuous Control](https://github.com/HROlive/Deep-Reinforcement-Learning-Nanodegree/tree/master/project_2-continuous_control): In the second project, I trained a robotic arm to reach target locations.

* [Collaboration and Competition](https://github.com/HROlive/Deep-Reinforcement-Learning-Nanodegree/tree/master/project_3-collaboration_competition): In the third project, I trained a pair of agents to play tennis! 

### Resources

* [Cheatsheet](https://github.com/udacity/deep-reinforcement-learning/blob/master/cheatsheet): You are encouraged to use [this PDF file](https://github.com/udacity/deep-reinforcement-learning/blob/master/cheatsheet/cheatsheet.pdf) 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__: 

	```bash

	conda create --name drlnd python=3.6

	source activate drlnd

	```

	- __Windows__: 

	```bash

	conda create --name drlnd python=3.6 

	activate drlnd

	```

	

2. Follow the instructions in [this repository](https://github.com/openai/gym) to perform a minimal install of OpenAI gym.  

	- Next, install the **classic control** environment group by following the instructions [here](https://github.com/openai/gym#classic-control).

	- Then, install the **box2d** environment group by following the instructions [here](https://github.com/openai/gym#box2d).

	

3. Clone the repository (if you haven't already!), and navigate to the `python/` folder.  Then, install several dependencies.

```bash

git clone https://github.com/udacity/deep-reinforcement-learning.git

cd deep-reinforcement-learning/python

pip install .

```

4. Create an [IPython kernel](http://ipython.readthedocs.io/en/stable/install/kernel_install.html) for the `drlnd` environment.  

```bash

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. 

![Kernel][image2]