felixrlopezm / Udacity_Deep_Reinforcement_Learning

My solutions to the examples, micro projects and projects of the Udaciy Deep Reinforcement Learning Nanodegree

Udacity Deep Reinforcement Learning

In this repository, I stored my implementations to the examples, micro projects and projects of the Udaciy Deep Reinforcement Learning Nanodegree program.

In general, I wrote my code using the starter code provided by Udacity as the take-off point and then modified it or added my whole own code where necessary. I also included additional remarks and comments to those already provided by Udacity for easing the understanding of the resulting code.

I started the nanodegree in August 2021 and finished in January 2022.

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. To install the base Gym library, use pip install gym. This basic instalation includes the classic control environment group and the box2d environment group.

   To install the Atari environment group, use conda and run: conda install -c conda-forge gym-atari

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.