Project 1: Navigation

Introduction

For this project, you will train an agent 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 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, your agent must get an average score of +13 over 100 consecutive episodes.

Requirements

• Conda
• jupyter-notebook

Getting Started

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. Clone the drl repository ( https://github.com/udacity/deep-reinforcement-learning).

git clone https://github.com/udacity/deep-reinforcement-learning.git

3. Clone this repository, replace requirements.txt in dlr repository file with requirements.txt file from this repository. (To fix Dependency issues for Tensorflow, Torch, ...) Then install dependencies.

git clone https://github.com/luctrate/drl_p1_navigation.git
cp drl_p1_navigation/requirements.txt deep-reinforcement-learning/python/requirements.txt 
cd deep-reinforcement-learning/python/
pip install .

4. Create an IPython kernel for the drlnd environment and then change directory o this repository.

python -m ipykernel install --user --name drlnd --display-name "drlnd"
cd ../../drl_p1_navigation/

5. 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

6. Place the file in this GitHub repository, in the root folder, and unzip (or decompress) the file.

7. Open Jupyter notebook.

jupyter-notebook

The browser should open a new tab with jupyter opened. If not check console output.

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

Instructions

Follow the instructions in Navigation.ipynb to use the trained agent or train the agent from sratch.