/the_apples_game

Multi-Agent Learning assignment, Machine Learning Project @ KU Leuven

Primary LanguageJavaScript

Apples game application

Several agents roam a shared world and collect apples to receive positive rewards. They may also direct a beam at one of the other agents, “tagging them”, to temporarily remove them from the game, but this action does not trigger a reward. https://deepmind.com/blog/understanding-agent-cooperation/

This setup is part of the course "Machine Learning: Project" (KU Leuven, Faculty of engineering, Department of Computer Science, DTAI research group).

Live demo: https://people.cs.kuleuven.be/~wannes.meert/the_apples_game/dist/

Screenshot of the Apples Game

Installation

The example agent is designed for Python 3.6 and requires the websockets package. Dependencies can be installed using pip:

$ pip install -r requirements.txt

Start the game GUI

This program shows a web-based GUI to play the Apples game. This supports human-human, agent-human and agent-agent combinations. It is a simple Javascript based application that runs entirely in the browser. You can start it by opening the file dist/index.html in a browser. Or alternatively, you can start the app using the included simple server:

$ ./server.py 8080

The game can then be played by directing your browser to http://127.0.0.1:8080.

Alternatively, you could run the game headless from the CLI. Therefore, you should first install the required dependencies:

$ npm install

Next, run the following command:

$ node play.js ws://localhost:8001 ws://localhost:8002 3

The arguments are the websocket addresses of the game-playing agents (as many as you want) and the number of apple patches (optional; default is 5). The agents are described in the next section.

Start the agent client

This is the program that runs a game-playing agent. This application listens to websocket requests that communicate game information and sends back the next action it wants to play.

Starting the agent client is done using the following command:

$ ./agent.py <port>

This starts a websocket on the given port that can receive JSON messages.

The JSON messages given below should be handled by your agent.

Initiate the game

Each agent gets a message that a new game has started:

{
    "type": "start",
    "player": 1,
    "game": "123456",
    "grid": [36, 16],
    "players": [
      {
        "location": [7, 10],
        "orientation": "right"
      },
      {
        "location": ["?", "?"],
        "orientation": "?"
      }
    ],
    "apples": [[10, 15], [3, 5], ...],
}

where player is the number assigned to this agent and grid is the grid size in columns and rows. Furthermore, players contains the initial locations and orientations of all players that participate. Finally, apples contains the locations of the apples. Locations are represented as [x, y] coordinates with the origin at the top left. Note that only the locations of apples that are within a 15x15 window of the agent's location are included in the message. Similarly, the location and orientation of the players that are outside this window are replaced with a question mark.

If you are player 1, reply with the first action you want to perform:

{
    "type": "action",
    "action": "move"
}

The field orientation is either 'move' (move on position forward), 'left' (turn left) 'right' (turn right) or 'fire' (fire at one the other agents).

Action in the game

When an action is played, each agent receives a message of the following format:

{
    "type": "action",
    "player": 1,
    "nextplayer": 2,
    "game": "123456",
    "players": [
      {
        "location": [7, 10],
        "orientation": "right",
        "score": 10
      },
      {
        "location": [33, 10],
        "orientation": "left",
        "score": 5
      }
    ],
    "apples": [[25, 15], [25, 14], ...],
    "receiver": 2
}

The field player corresponds to the ID of the player that executed the action, the nextplayer field indicates which player should play the next turn and the receiver field has the ID of the player that should receive this message. Finally, the fields players and apples hold information about respectively the other players and the apples in the field. However, the fields players and apples will be different for each receiving agent, depending on each agent's 15x15 observable window.

If it is your turn you should answer with a message that states your next move:

{
    "type": "action",
    "action": "fire"
}

Game end

When the game ends after an action, the message is slightly altered:

{
    "type": "end",
    "game": "123456",
    "player": 1,
    "nextplayer": 0,
    "players": [
      {
        "location": [7, 10],
        "orientation": "right",
        "score": 10
      },
      {
        "location": [33, 10],
        "orientation": "left",
        "score": 5
      }
    ],
    "apples": [[25, 15], [25, 14], ...]
    "winner": 1,
    "receiver": 2
}

The type field becomes end and a new field winner is set to the player that has won the game.

Modify the agent client

The agent client is written using the Phaser 3 framework. A pre-compiled version is available in the dist folder. However, if you would like to modify this client you have to install the required dependencies:

npm install --global gulp-cli
npm install

Next use the commands gulp to start the development server and gulp dist to create a new compiled version in the dist folder.

Contact information

Main developer:

Team: