Multi-Agent Gridworld Environment (MultiGrid)

For overall project summary click here

Installation

How to run:

clone and cd into the repo and run the following command:

pip install -e .

specify the path to the competition .json files in mapf_env_test.py the environment will load the map, agent and task files from the relative path provided in the .json

python3 mapf_env_test.py

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The repo for maps can be found here

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Lightweight multi-agent gridworld Gym environment built on the MiniGrid environment.

Requirements:

• Python 3.5+
• OpenAI Gym
• NumPy
• Matplotlib

Design

The environment can be either fully or partially observable. Each grid cell is encoded with a tuple containing:

• The type of the object (can be another agent)
  • Provided object types are: wall, floor, lava, door, key, ball, box, goal, object goal and agent
• The color of the object or other agent
• The type of the object that the other agent is carrying
• The color of the object that the other agent is carrying
• The direction of the other agent
• Whether the other agent is actually one-self (useful for fully observable view)

Actions in the basic environment:

• Turn left
• Turn right
• Move forward
• Done (task completed, optional)