/Navigation_Bananas_DRL

Primary LanguagePythonMIT LicenseMIT

Deep Q learning to solve a Navigation Problem

Introduction

In this project, an agent will be trained to navigate (and collect bananas!) in a large, square world.

Trained Agent

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.

Getting Started

Dependencies

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.

  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. 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.

  3. 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 .
  1. 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.

Environment

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

    (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.

  2. Place the file in the previously created/cloned DRLND GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file.

Instructions

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!