For this project, an agent trains 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 agent is to collect as many yellow bananas as possible while avoiding blue bananas in a max 300 steps.

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, agent must get an average score of +13 over 100 consecutive episodes.

1. Clone this GitHub repository (p1_navigation). If you do not use Linux, go to step 2., if you do, go to step 4.

2. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • 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.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.

3. Place the file in this GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file and delete Banana_Linux folder.

4. Open new terminal and run your virtual environment with Python3 : $source activate drlnd

5. In terminal, place yourself inside of p1_navigation folder

6. Run notebook: $jupyter notebook Navigation.ipynb

Follow the instructions in Navigation.ipynb to get started with training your own agent!