Go-Bigger: Multi-Agent Decision Intelligence Environment

GoBigger Doc (中文版)

GoBigger is an efficient and straightforward agar-like game engine and provides various interfaces for game AI development. The game is similar to Agar, a massive multiplayer online action game created by Brazilian developer Matheus Valadares. In GoBigger, players control one or more circular balls on a map. The goal is to gain as much size as possible by eating Food Balls and other balls smaller than the player's balls while avoiding larger ones that can eat the player's balls. Each player starts with one ball, and players can split a ball into two when it reaches a sufficient size, allowing them to control multiple balls.

We pay more attention to the following points:

• Complex multi-agent game design
• Complex observation spaces built on simple rules and simple actions
• More detailed configuration
• Custom opening with support for go-explore

Outline

• Overview
• Getting Start
• Resources
• Join and Contribute
• License

Overview

GoBigger allows users to to interact with the multi-agent environment within the basic rules easily. Users can simply get the observation in the game and apply their operations for their agents through the given interface. GoBigger is built with simple rules and actions, though it has complicated observation spaces.

Basic Rules

To understand the rules in the game, GoBigger provides a few concepts as follows:

• Match: GoBigger will allow several agents (4 by default) to join in a match. There are many different units in a match, such as food balls, thorns balls, spore balls and player balls. When this match ends, each agent should gain more size by eating other balls to get a higher rank.
• Agent: Each agent control a team, including several players (3 by default). Teamwork is essential for an agent to play against other agents.
• Player: Each player starts with one ball. To improve the operability of the game, GoBigger provides several operations for a player ball, including split, eject and stop.
• Ball: GoBigger provides 4 kinds of balls in a match.
  • Food Ball: Food balls are the neutral resources in the game. If a player ball eats a food ball, the food ball’s size will be parsed to the player ball.
  • Thorn Ball: If a player ball eats a thorns ball, the thorns ball’s size will be parsed to the player ball. But at the same time, the player ball will explode and split into several pieces (10 by default).
  • Spore Ball: Spore balls are ejected by the player balls.
  • Player Ball: Player balls are the balls you can control in the game. You can change its moving direction. In addition, it can eat other balls smaller than itself by covering others’ centers.

For more details, please refer to what-is-gobigger.

Observation Space

GoBigger also provides a wealth of observable information, and the observation space can be divided into two parts. Here is a brief description of the observation space. For more details, please refer to observation-space.

Global State

The global state provides information related to the whole match, such as the map size, the total time and the last time of the match, and the leaderboard with team name and score.

Player State

The player state should be like:

{
    player_name: {
        'feature_layers': list(numpy.ndarray), # features of player
        'rectangle': [left_top_x, left_top_y, right_bottom_x, right_bottom_y], # the vision's position in the map
        'overlap': {
            'food': [{'position': position, 'radius': radius}, ...], # the length of food is not sure
            'thorns': [{'position': position, 'radius': radius}, ...], # the length of food is not sure
            'spore': [{'position': position, 'radius': radius}, ...], # the length of food is not sure
            'clone': [{'position': position, 'radius': radius, 'player': player_name, 'team': team_name}, ...], # the length of food is not sure
        }, # all balls' info in vision
        'team_name': team_name, # the team which this player belongs to 
    }
}

We define that feature_layers in player_state represent the feature of this player. feature_layers has several channels, and each channel gives the info of food balls, spore balls, thorns balls, or player balls in its vision. For example, in a match, we have 4 teams and 3 players for each team, then we get feature_layers as a list, and the length of this list should be 15, including 12 player channels, 1 food ball channel, 1 spore ball channel and 1 thorns ball channel.

Since getting feature_layers costs much time, GoBigger also provides player state without feature_layers when you add use_spatial=False in your render. More details here.

Action Space

In fact, a ball can only move, eject, split, and stop in a match; thus, the action space simply includes:

• Moving direction for the player balls.
• Split: Players can split a ball into two when it reaches a sufficient size.
• Eject: Player balls can eject spore in your moving direction.
• Stop: Stop player balls and gather together.

More details in action-space.

Getting Start

Setup

We test GoBigger within the following system:

• Centos 7.6
• Windows 10
• MacOS Catalina 10.15

And we recommend that your python version is 3.6.

Installation

You can simply install GoBigger from PyPI with the following command:

pip install gobigger

If you use Anaconda or Miniconda, you can install GoBigger through the following command:

conda install -c opendilab gobigger

You can also install the newest version through GitHub. First, get and download the official repository with the following command line.

git clone https://github.com/opendilab/GoBigger.git

Then you can install it from the source:

# install for use
# Note: use `--user` option to install the related packages in the user own directory(e.g.: ~/.local)
pip install . --user
     
# install for development(if you want to modify GoBigger)
pip install -e . --user

Usage

After installation, you can launch your game environment easily according the following code:

import random
from gobigger.server import Server
from gobigger.render import EnvRender

server = Server()
render = EnvRender(server.map_width, server.map_height)
server.set_render(render)
server.reset()
player_names = server.get_player_names_with_team()
# get [[team1_player1, team1_player2], [team2_player1, team2_player2], ...]
for i in range(10000):
    actions = {player_name: [random.uniform(-1, 1), random.uniform(-1, 1), -1] \
               for team in player_names for player_name in team}
    if not server.step(actions):
        global_state, screen_data_players = server.obs()
        print('[{}] leaderboard={}'.format(i, global_state['leaderboard']))
    else:
        print('finish game!')
        break
server.close()

You will see output as follows. It shows the frame number and the leaderboard per frame.

[0] leaderboard={'0': 27, '1': 27, '2': 27, '3': 27}
[1] leaderboard={'0': 27, '1': 27, '2': 27, '3': 27}
[2] leaderboard={'0': 27, '1': 30.99935, '2': 30.99935, '3': 30.998700032499997}
[3] leaderboard={'0': 27, '1': 34.99610032498374, '2': 34.99675032498374, '3': 30.9961004874675}
[4] leaderboard={'0': 27, '1': 38.99025149484726, '2': 34.99155136485701, '3': 38.992201494805016}
[5] leaderboard={'0': 30.998700032499997, '1': 42.982054039382575, '2': 34.98635344444432, '3': 38.98620350437408}
[6] leaderboard={'0': 34.9961004874675, '1': 42.973458273284024, '2': 34.98115656353774, '3': 38.98020671345127}
[7] leaderboard={'0': 34.99270152230301, '1': 46.964264256209255, '2': 38.974660754429394, '3': 38.974211121796685}
[8] leaderboard={'0': 34.98930323688059, '1': 46.954872107798515, '2': 38.96686640687893, '3': 38.96821672917049}
[9] leaderboard={'0': 38.98525563106426, '1': 46.94548183767656, '2': 38.95907361808107, '3': 38.96222353533294}
...

We also build a simple env following gym.Env. For more details, you can refer to gobigger_env.py.

Real-time Interaction with the Game

GoBigger allows users to play the game on their personal computers in real-time. Several modes are supported for users to explore this game.

Single Player

If you want to play real-time game on your PC on your own, you can launch a game with the following code:

python -m gobigger.bin.play --player-num 1 --vision-type full

In this mode, the up arrow & down arrow & left arrow & right arrow allows your balls to move, Q means to eject spore in your moving direction, W means to split your balls, and E means to stop all your balls and gather them together.

Double Players

If you want to play the real-time game on your PC with your friends, you can launch a game with the following code:

python -m gobigger.bin.play --player-num 2 --vision-type full

In this mode, player1 uses the up arrow & down arrow & left arrow & rigth arrow to allow the balls to move, [ means eject spore in your moving direction, ] means split your balls, and \ means stop all your balls and gather them together. Player2 uses W & S & A & D to allow the balls move, 1 means eject spore in your moving direction, 2 means split your balls, and 3 means stop all your balls and gather them together.

Single Players with Partial Vision

If you want to play the real-time game on your PC with only partial vision, you can launch a game with the following code:

python -m gobigger.bin.play --player-num 1 --vision-type partial

Your vision depends on all your balls’ positions and their size.

Single Players Against Bots

If you want to play against a bot, you can launch a game with the following code:

python -m gobigger.bin.play --vs-bot

You can also add more bots to your game. Try to win the game with more bots!

python -m gobigger.bin.play --vs-bot --team-num 4

High-level Operations in GoBigger

Eject towards the center

Surround others by splitting

Eat food balls quickly

Concentrate size

Resources

For more details, please refer to GoBigger Doc (中文版).

Join and Contribute

Welcome to OpenDI Lab GoBigger community! Scan the QR code and add us on Wechat:

Or you can contact us with slack or email (opendilab.contact@gmail.com).

License

GoBigger released under the Apache 2.0 license.