Maze Unity

This is a graphical simulation developed in Unity based on the real world experiment from [1] and used from the H-AI_collab_game

This forked from https://github.com/panos-stavrianos/MazeUnity and adjusted accordingly for our purposes.

The environment receives actions (plus other important information) and sends back observations (plus other important information) to H-AI_collab_game.

The above messages are being exchanged via an HTTP server.

H-AI_collab_game and maze_GI_Unity work as HTTP clients.

Edit MazeUnity

• Download git

  git clone https://github.com/ligerfotis/MazeUnity.git
  

• Install Unity (Version: 2020.3.13f1)

• Open with unity

  • If Unity cannot open the project or does not recognize it, create a new project and replace the folders.

• Remove default scene

• Drag and drop main scene from Scenes in the Hierarchy

Add Rider in unity (Recommended for editting)

https://www.jetbrains.com/help/rider/Unity.html#getting-started

Play

Prerequisites

• Start the dedicated Maze Server
• Start the experiment MazeRL

Play in Unity Editor

Just open the game in Unity and press the play button. See here for connection instructions

Play in Web Browser

Every time a user opens the link to the webgl in the browser the game is being sent to it from a docker.

Start webgl server with docker

• Build Settings -> web_build -> (switch platform) -> build

• Choose web_build and name it “webgl”

• Go to to MazeUnity/web_build

    cd MazeUnity/web_build
  

• Edit webgl.conf 'server_name' to the name of your server without the ‘http://’ header (default: localhost)

• Make sure the ports used below are open.

•   docker build -t <image_name>:<version> . (docker build -t maze-unity:1.0.0 .)
  

•   docker run -p <host_port>:80 <image_name>:<image_version> (docker run -p 12000:80 maze-unity:1.0.0)
  

• Open <server_name>:<host_port> (localhost:12000) in a browser.

• If you want to stop the docker: docker stop maze

• If you want to remove the docker: docker rm maze

HTTP connectivity

In Assets/Scripts folder.

MazeUnity Environment Overview

Dimensionality

• Tray: 50cm x 50cm
• Wall thickness: 3cm
• Obstacles opening: 9 cm
• Ball radius: 5cm
• Hole radius: 5 cm

References

[1] Shafti, Ali, et al. "Real-world human-robot collaborative reinforcement learning." arXiv preprint arXiv:2003.01156 (2020).