/DQN-Unity-agent

In this notebook, you will learn how to use the Unity ML-Agents environment to navigate the agent across the environment increasing its probabilty of hight rewards with constant exploration of new tasks

Primary LanguageJupyter NotebookMIT LicenseMIT

Environment

The aim of this project is to train an agent to navigate collect yellow bananas while avoiding blue bananas.A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana.

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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, the agent must get an average score of +13 over 100 consecutive episodes.

Getting started

  1. Activate the environment:

    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.

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

Instructions

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