MiniGrid — Minimalistic Gridworld Environments

MiniGrid — Minimalistic Gridworld Environments

MiniGrid is a collection of fast, minimalistic grid-world environments for reinforcement learning research, maintained by the Farama Foundation. Environments are partially observable, goal-conditioned, and designed to test key capabilities of intelligent agents: navigation, object manipulation, memory, planning, and instruction following. BabyAI (included) extends MiniGrid with natural language instruction following.

Key Features

• 30+ gridworld environments with randomized level generation
• Partial observability: agents see a small ego-centric view of the grid
• Object types: doors, keys, balls, boxes — enabling pick-up, unlock, and carry tasks
• Mission strings: natural language goal descriptions for language-conditioned RL
• Extremely fast: Python-only, no C++ — thousands of steps/second
• Gymnasium-compatible API

Quick Start

pip install minigrid

import gymnasium as gym

env = gym.make("MiniGrid-DoorKey-8x8-v0", render_mode="human")
obs, info = env.reset()
print("Mission:", obs["mission"])  # e.g. "open the door"

for _ in range(500):
    action = env.action_space.sample()  # 0-6: turn left/right, forward, pickup, drop, toggle, done
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()
env.close()

npx ai-supply add minigrid-gridworld-environments

Curated mirror of the open-source MiniGrid (Apache-2.0). Get it from the source.