Miniworld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research

Sequence of observations from Collect Health environment

MiniWorld allows environments to be easily edited like Minigrid meets DM Lab. It can simulate environments with rooms, doors, hallways, and various objects (e.g., office and home environments, mazes).

Installation#

pip install miniworld

Usage#

The Gymnasium interface allows to initialize and interact with the Miniworld default environments as follows:

import gymnasium as gym
env = gym.make("MiniWorld-OneRoom-v0")
observation, info = env.reset(seed=42)
for _ in range(1000):
   action = policy(observation)  # User-defined policy function
   observation, reward, terminated, truncated, info = env.step(action)

   if terminated or truncated:
      observation, info = env.reset()
env.close()

Citation#

To cite this project please use:

@article{MinigridMiniworld23,
  author       = {Maxime Chevalier-Boisvert and Bolun Dai and Mark Towers and Rodrigo de Lazcano and Lucas Willems and Salem Lahlou and Suman Pal and Pablo Samuel Castro and Jordan Terry},
  title        = {Minigrid \& Miniworld: Modular \& Customizable Reinforcement Learning Environments for Goal-Oriented Tasks},
  journal      = {CoRR},
  volume       = {abs/2306.13831},
  year         = {2023},
}