In this project, an agent is trained that navigates (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 the 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, your agent must get an average score of +13 over 100 consecutive episodes.

1. Download the Unity 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.

   (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.

2. Clone this code running the command git clone https://github.com/dmavridis/DQN-Navigation.git and navigate to the root folder.

3. • Create a new environment conda create --name drlnd python=3.6
   • Load the environment source activate drlnd
   • cd DQN-Navigation

4. Install the necessary python packages by running pip install .

5. Extract the downloaded Banana ***.zip Unity environment executeble at the folder of the python environment

Follow the instructions in report.ipynb to see the required steps to run the environment in interactive mode.