Random maze environments with different size and complexity for reinforcement learning and planning research. This is in-particular to investigate generalization and planning ability in dynamically changing environment.
Project description
maze-world
Random maze environments with different size and complexity for reinforcement learning and planning research. This is in-particular to investigate generalization and planning ability in dynamically changing environment.
Disclaimer: This project is largely a amalgam of references mentioned here.
Installation
-
Using PyPI:
pip install maze-world
-
Directly from source (recommended):
git clone https://github.com/koulanurag/maze-world.git cd maze-world pip install -e .
Environments Zoo!
RandomMaze-11x11-v0 | RandomMaze-21x21-v0 | RandomMaze-31x31-v0 | RandomMaze-101x101-v0 |
See all here.
Quick-Start:
import gymnasium as gym
env = gym.make("maze_world:RandomMaze-11x11-v0", render_mode="human")
terminated, truncated = False, False
observation, info = env.reset(seed=0, options={})
episode_score = 0.
while not (terminated or truncated):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
episode_score += reward
env.close()
See entire quick-start guide here.
Testing:
- Install:
pip install -e ".[test]"
- Run:
pytest
Development:
If you would like to develop it further; begin by installing following:
pip install -e ".[develop]"
References:
Project details
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