In this project, an agent will be trained to navigate (and collect bananas!) in a large, square world.
A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to:
0
- move forward.1
- move backward.2
- turn left.3
- turn right.
The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.
To set up the python environment to run the code in this repository, the easiest way is to import the project environment in your conda.
- Linux, Ubuntu 18.04 LTS:
conda env create -f drlnd_environment.yml
If the above has not worked or is not a feasible solution, follow the instructions below.
-
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
-
Follow the instructions in this repository to perform a minimal install of OpenAI gym.
-
Clone this repository, 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 .
- If you are going to use Jupyter Notebook, create an IPython kernel for the
drlnd
environment.
python -m ipykernel install --user --name drlnd --display-name "drlnd"
Before running code in a notebook, change the kernel to match the drlnd
environment by using the drop-down Kernel
menu.
-
Download the environment from one of the links below. You need only select the environment that matches your operating system:
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
(For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.
-
Place the file in the previously created/cloned DRLND GitHub repository, in the
p1_navigation/
folder, and unzip (or decompress) the file.
Follow the instructions in Navigation.ipynb
to get started with training your own agent!
In the DRLND_P1_Report.pdf
you will find information about the implementation and the obtained results. Check it out and try it yourself!