YifanTian/udacity-deep-reinforcement-learning-navigation

Train an agent to navigate (and collect bananas!) in a large, square world using deep reinforcement learning. It is project 1 of Udacity deep-reinforcement-learning-navigation nanodegree

README

1. Project Details

The action space:
W - move forward.
S - move backward.
A - turn left.
D - turn right.

The state space:
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. Given this information, the agent has to learn how to best select actions.

2. Getting Started

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 .

3. Instructions

1. Open the IPython notebook.

jupyter notebook Navigation.ipynb

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