Skip to content

Latest commit

 

History

History
78 lines (56 loc) · 3.31 KB

README.md

File metadata and controls

78 lines (56 loc) · 3.31 KB
Raw

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 nav python=3.6
source activate nav
    • Windows:
    conda create --name nav python=3.6 
activate nav

  2. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

    git clone https://github.com/avin-sharma/Navigation-RL.git
cd Navigation-RL/python
pip install .

  3. Create an IPython kernel for the nav environment.

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

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

Navigation

Introduction

For this project, we train an agent 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 our 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, our agent must get an average score of +13 over 100 consecutive episodes.

Getting Started

  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 downloaded Navigation-RL GitHub repository, in the and unzip (or decompress) the file.

  3. Run the Jupyter Notebook and execute the cells in the notebook to run the project. To see the trained agent you may want to skip to where we load the network and run the unity environment.

    • Linux or Mac:
    source activate nav
jupyter notebook
    • Windows:
    activate nav
jupyter notebook