Udacity Deep Reinforcement Learning Nanodegree

Project 1: Navigation

 

Project Details

The goal of this project is to train a RL agent to navigate inside an square world collecting yellow bananas while avoiding blue ones.

The environment consists of a 37 dimensions state space which provides the agent's velocity along with an representation of the objects right in front of the agent. Moreover, the environment rewards the agent with +1 every time it collects a yellow banana, and -1 every time it collects a blue banana.

Every episode has a length of 300 steps, making it an episodic task.

The action space consists of four discrete actions presented here below:

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

Finally, the environment in considered solved when the agent manages to get an mean reward above 13 points in 100 episodes.

Getting Started

1. 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.

   (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. Place the file in this folder, unzip (or decompress) the file and then write the correct path in the argument for creating the environment under the notebook Navigation_solution.ipynb:

env = UnityEnvironment(file_name="Banana.app")

Instructions

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

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. If running in Windows, ensure you have the "Build Tools for Visual Studio 2019" installed from this site. This article may also be very helpful. This was confirmed to work in Windows 10 Home.

3. 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.

4. 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 .

5. Create an IPython kernel for the drlnd environment.

   python -m ipykernel install --user --name drlnd --display-name "drlnd"

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