Project 1: Navigation

Introduction

This project aims at training 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 at least +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:

   • 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 the root directory of this repository, and unzip (or decompress) the file.

3. Creating an environment from an conda-environment.yml file

   • Install Anaconda if necessary: https://conda.io/docs/user-guide/install/index.html
   • conda env create -f conda-environment.yml

4. Activate the new environment

   • Windows: activate drlnd
   • macOS and Linux: source activate drlnd

5. Verify that the new environment was installed correctly

   • conda list

Instructions

Follow the instructions in Report.ipynb to get started with training the agent!