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Project 1: Navigation

Introduction

For this project, an agent is trained to navigate in a large, square world. The agent must collect as many yellow bananas as possible in 300 time steps, while avoiding blue bananas.

Furthermore, a comparison between vanilla DQN and double DQN is presented.

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 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 the 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

  1. Clone Udacity's DRLND Repository If you haven't already, please follow the instructions in the DRLND GitHub repository to set up your Python environment. These instructions can be found in README.md at the root of the repository. By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project.

    (For Windows users) The ML-Agents toolkit supports Windows 10. While it might be possible to run the ML-Agents toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.

    NOTE: This project uses certain functionalities of PyTorch 1.1, which are not available in version 0.4.0, the version installed with Udacity's DRLND repository. Therefore, after setting up everything and assuming you're using conda, make sure you run the following commands:

    pip uninstall torch
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

    This will install the latest version of PyTorch.

  2. 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.

    (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.

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

Instructions

Follow the instructions in Navigation.ipynb to get started with training your own agent!