For this project, you will train an agent to navigate (and collect bananas!) in a large, square world.

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. Four discrete actions are available, corresponding to:

0 - move forward.
1 - move backward.
2 - turn left.
3 - turn right.

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 task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.

1. If you haven't already, please clone the DRLND GitHub repository.
2. Navigate to the p1_navigation/ folder in the DRLND GitHub repository, and run the command below to obtain a few packages.

pip3 install -r requirements.txt

Next, please 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

Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file.

(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.

1. Clone the ML-Agents repository, and enter the python/ subdirectory:

    git clone https://github.com/Unity-Technologies/ml-agents.git
    cd ml-agents/python

2. Run the following command in a terminal to install the necessary dependencies:

    pip3 install .

After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.

Follow the instructions in Navigation.ipynb to get started with training your own agent! To use a Jupyter notebook, run the following command from the p1_navigation/ folder:

jupyter notebook

and open Navigation.ipynb from the list of files. Alternatively, you may prefer to work with the JupyterLab interface. To do this, run this command instead:

jupyter-lab