Project 1: Navigation

Introduction

This project uses Deep Q Network(DQN) to train an agent to navigate a large, square world to collect yellow bananas and avoid blue bananas. This project is part of Deep Reinforcement Learning Nanodegree.

This environment is a simplified version of the ML Agents - Banana Collector environment.

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

Pre-requisites

• Make sure you having a working version of Anaconda on your system.

Step 1: Create and activate Conda environment

Create (and activate) a new environment with Python 3.6.

- __Linux__ or __Mac__: 
```bash
conda create --name drlnd python=3.6
source activate drlnd
```
- __Windows__: 
```bash
conda create --name drlnd python=3.6 
activate drlnd
```

Step 2: Clone the repo

Clone this repo using https://github.com/sriramjaju/deep-q-network.git. Navigate to the python/ folder. Then, install several dependencies.

Step 3: Download Banana environment

You will also need to install the pre-built Unity environment, you will NOT need to install Unity itself. Select the appropriate file for your operating system:

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 repository, and unzip (or decompress) the file.

Instructions

Follow the instructions in Navigation.ipynb (or Navigation-cpu.ipynb if you are training this on the cpu) to get started with training your own agent!