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.

Getting Started

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

Dependencies

Python 3.6
PyTorch 0.4.0
Unity Agents
Numpy
Matplotlib
Tqdm

(You just need to have Python 3.6. Rest is being installed from the Navigation.ipynb file.)

Instructions

This work consists of 4 essential scripts:

agent.py: Consists of agent behaviour and related functions
dqn_utils.py: Consists of training procedures and other future training related utils functions
model.py: Consists of the neural network employed within the agent
Navigation.ipynb: Where all parts come together

Please note that you need a proper Unity environment for your PC. If you do not have the environment, please download the one fits for your system from the Getting Started section.

How to run the code

Create an environment. Example anaconda environment creation:

conda create -n project_bananas python==3.6
conda activate project_bananas

Open the Navigation.ipynb file.
If it's your first run with your freshly created environment, run the first cell. You can skip this one if it isn't your first run with the code.
Run rest of the cells.

Further report, obtained results, future improvements and ideas can be seen in Report.html file.

HAVE A NICE ONE!

iamkucuk/udacity_p1