| Name | miniworld-maze JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | Multi-room maze environments from the DrStrategy paper for reinforcement learning research |
| upload_time | 2025-09-02 08:29:49 |
| maintainer | None |
| docs_url | None |
| author | Tim Joseph |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 Tim Joseph
Permission is hereby granted, free of charge, to any person obtaining a copy
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| keywords |
reinforcement-learning
environment
gymnasium
multi-room-maze
drstrategy
maze-navigation
partial-observability
3d-environments
|
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# MiniWorld DrStrategy - Multi-Room Maze Environment
A refactored implementation of Dr. Strategy's MiniWorld-based maze environments with updated dependencies and modern Python packaging. Based on the now-deprecated [MiniWorld](https://github.com/Farama-Foundation/Miniworld) project and the original [DrStrategy implementation](https://github.com/ahn-ml/drstrategy).
## Environment Observations
### Environment Views
Full environment layout and render-on-position views:
| Full Environment | Partial Top-Down Observations | Partial First-Person Observations |
|---|---|---|
|  |   |   |
## Installation
```bash
pip install miniworld-maze
```
## Usage
### Basic Usage
See `examples/basic_usage.py` for a complete working example:
```python
#!/usr/bin/env python3
"""
Basic usage example for miniworld-maze environments.
This is a minimal example showing how to create and interact with the environment.
"""
from miniworld_maze import NineRoomsEnvironmentWrapper
def main():
# Create environment
env = NineRoomsEnvironmentWrapper(variant="NineRooms", size=64)
obs, info = env.reset()
# obs is a dictionary containing:
# - 'observation': (64, 64, 3) RGB image array
# - 'desired_goal': (64, 64, 3) RGB image of the goal state
# - 'achieved_goal': (64, 64, 3) RGB image of the current state
# Take a few random actions
for step in range(10):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
print(f"Step {step + 1}: reward={reward:.3f}, terminated={terminated}")
if terminated or truncated:
obs, info = env.reset()
env.close()
print("Environment closed successfully!")
if __name__ == "__main__":
main()
```
### Headless Environments
When running in headless environments (servers, CI/CD, Docker containers) or when encountering X11/OpenGL context issues, you need to enable headless rendering:
```bash
# Set environment variable before running Python
export PYGLET_HEADLESS=1
python your_script.py
```
Or in your Python code (must be set before importing the library):
```python
import os
os.environ['PYGLET_HEADLESS'] = '1'
import miniworld_maze
# ... rest of your code
```
This configures the underlying pyglet library to use EGL rendering instead of X11, allowing the environments to run without a display server.
## Environment Variants
### Available Environments
The package provides three main environment variants, each with different room layouts and connection patterns:
#### 1. NineRooms (3×3 Grid)
```
-------------
| 0 | 1 | 2 |
-------------
| 3 | 4 | 5 |
-------------
| 6 | 7 | 8 |
-------------
```
A standard 3×3 grid where adjacent rooms are connected. The agent can navigate between rooms through doorways, with connections forming a fully connected grid pattern.
#### 2. SpiralNineRooms (3×3 Spiral Pattern)
```
-------------
| 0 | 1 | 2 |
-------------
| 3 | 4 | 5 |
-------------
| 6 | 7 | 8 |
-------------
```
Same room layout as NineRooms but with a spiral connection pattern. Only specific room pairs are connected, creating a more challenging navigation task with fewer available paths.
#### 3. TwentyFiveRooms (5×5 Grid)
```
---------------------
| 0 | 1 | 2 | 3 | 4 |
---------------------
| 5 | 6 | 7 | 8 | 9 |
---------------------
|10 |11 |12 |13 |14 |
---------------------
|15 |16 |17 |18 |19 |
---------------------
|20 |21 |22 |23 |24 |
---------------------
```
A larger 5×5 grid environment with 25 rooms, providing more complex navigation challenges and longer episode lengths.
### Observation Types
Each environment supports three different observation modes:
- **`TOP_DOWN_PARTIAL`** (default): Agent-centered partial top-down view with limited visibility range (POMDP)
- **`TOP_DOWN_FULL`**: Complete top-down view showing the entire environment
- **`FIRST_PERSON`**: 3D first-person perspective view from the agent's current position
### Action Space
- **Discrete Actions** (default): 7 discrete actions (turn left/right, move forward/backward, strafe left/right, no-op)
- **Continuous Actions**: Continuous control with `continuous=True` parameter
### Environment Configuration
All environments can be customized with the following parameters:
```python
from miniworld_maze import NineRoomsEnvironmentWrapper
from miniworld_maze.core import ObservationLevel
env = NineRoomsEnvironmentWrapper(
variant="NineRooms", # "NineRooms", "SpiralNineRooms", "TwentyFiveRooms"
obs_level=ObservationLevel.TOP_DOWN_PARTIAL, # Observation type
continuous=False, # Use continuous actions
size=64, # Observation image size (64x64)
room_size=5, # Size of each room in environment units
door_size=2, # Size of doors between rooms
agent_mode="empty", # Agent rendering: "empty", "circle", "triangle"
)
```
### Observation Format
The environment returns observations in dictionary format:
```python
obs = {
'observation': np.ndarray, # (64, 64, 3) RGB image of current view
'desired_goal': np.ndarray, # (64, 64, 3) RGB image of goal location
'achieved_goal': np.ndarray, # (64, 64, 3) RGB image of current state
}
```
### Reward Structure
- **Goal reaching**: Positive reward when agent reaches the goal location
- **Step penalty**: Small negative reward per step to encourage efficiency
- **Episode termination**: When goal is reached or maximum steps exceeded
## License
MIT License - see LICENSE file for details.
Raw data
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"description": "# MiniWorld DrStrategy - Multi-Room Maze Environment\n\nA refactored implementation of Dr. Strategy's MiniWorld-based maze environments with updated dependencies and modern Python packaging. Based on the now-deprecated [MiniWorld](https://github.com/Farama-Foundation/Miniworld) project and the original [DrStrategy implementation](https://github.com/ahn-ml/drstrategy).\n\n## Environment Observations\n\n### Environment Views\nFull environment layout and render-on-position views:\n\n| Full Environment | Partial Top-Down Observations | Partial First-Person Observations |\n|---|---|---|\n|  |   |   |\n\n## Installation\n\n```bash\npip install miniworld-maze\n```\n\n## Usage\n\n### Basic Usage\n\nSee `examples/basic_usage.py` for a complete working example:\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nBasic usage example for miniworld-maze environments.\n\nThis is a minimal example showing how to create and interact with the environment.\n\"\"\"\n\nfrom miniworld_maze import NineRoomsEnvironmentWrapper\n\n\ndef main():\n # Create environment\n env = NineRoomsEnvironmentWrapper(variant=\"NineRooms\", size=64)\n obs, info = env.reset()\n\n # obs is a dictionary containing:\n # - 'observation': (64, 64, 3) RGB image array\n # - 'desired_goal': (64, 64, 3) RGB image of the goal state\n # - 'achieved_goal': (64, 64, 3) RGB image of the current state\n\n # Take a few random actions\n for step in range(10):\n action = env.action_space.sample()\n obs, reward, terminated, truncated, info = env.step(action)\n\n print(f\"Step {step + 1}: reward={reward:.3f}, terminated={terminated}\")\n\n if terminated or truncated:\n obs, info = env.reset()\n\n env.close()\n print(\"Environment closed successfully!\")\n\n\nif __name__ == \"__main__\":\n main()\n```\n\n### Headless Environments\n\nWhen running in headless environments (servers, CI/CD, Docker containers) or when encountering X11/OpenGL context issues, you need to enable headless rendering:\n\n```bash\n# Set environment variable before running Python\nexport PYGLET_HEADLESS=1\npython your_script.py\n```\n\nOr in your Python code (must be set before importing the library):\n\n```python\nimport os\nos.environ['PYGLET_HEADLESS'] = '1'\n\nimport miniworld_maze\n# ... rest of your code\n```\n\nThis configures the underlying pyglet library to use EGL rendering instead of X11, allowing the environments to run without a display server.\n\n## Environment Variants\n\n### Available Environments\n\nThe package provides three main environment variants, each with different room layouts and connection patterns:\n\n#### 1. NineRooms (3\u00d73 Grid)\n```\n-------------\n| 0 | 1 | 2 |\n-------------\n| 3 | 4 | 5 |\n-------------\n| 6 | 7 | 8 |\n-------------\n```\nA standard 3\u00d73 grid where adjacent rooms are connected. The agent can navigate between rooms through doorways, with connections forming a fully connected grid pattern.\n\n#### 2. SpiralNineRooms (3\u00d73 Spiral Pattern)\n```\n-------------\n| 0 | 1 | 2 |\n-------------\n| 3 | 4 | 5 |\n-------------\n| 6 | 7 | 8 |\n-------------\n```\nSame room layout as NineRooms but with a spiral connection pattern. Only specific room pairs are connected, creating a more challenging navigation task with fewer available paths.\n\n#### 3. TwentyFiveRooms (5\u00d75 Grid)\n```\n---------------------\n| 0 | 1 | 2 | 3 | 4 |\n---------------------\n| 5 | 6 | 7 | 8 | 9 |\n---------------------\n|10 |11 |12 |13 |14 |\n---------------------\n|15 |16 |17 |18 |19 |\n---------------------\n|20 |21 |22 |23 |24 |\n---------------------\n```\nA larger 5\u00d75 grid environment with 25 rooms, providing more complex navigation challenges and longer episode lengths.\n\n### Observation Types\n\nEach environment supports three different observation modes:\n\n- **`TOP_DOWN_PARTIAL`** (default): Agent-centered partial top-down view with limited visibility range (POMDP)\n- **`TOP_DOWN_FULL`**: Complete top-down view showing the entire environment\n- **`FIRST_PERSON`**: 3D first-person perspective view from the agent's current position\n\n### Action Space\n\n- **Discrete Actions** (default): 7 discrete actions (turn left/right, move forward/backward, strafe left/right, no-op)\n- **Continuous Actions**: Continuous control with `continuous=True` parameter\n\n### Environment Configuration\n\nAll environments can be customized with the following parameters:\n\n```python\nfrom miniworld_maze import NineRoomsEnvironmentWrapper\nfrom miniworld_maze.core import ObservationLevel\n\nenv = NineRoomsEnvironmentWrapper(\n variant=\"NineRooms\", # \"NineRooms\", \"SpiralNineRooms\", \"TwentyFiveRooms\"\n obs_level=ObservationLevel.TOP_DOWN_PARTIAL, # Observation type\n continuous=False, # Use continuous actions\n size=64, # Observation image size (64x64)\n room_size=5, # Size of each room in environment units\n door_size=2, # Size of doors between rooms \n agent_mode=\"empty\", # Agent rendering: \"empty\", \"circle\", \"triangle\"\n)\n```\n\n### Observation Format\n\nThe environment returns observations in dictionary format:\n\n```python\nobs = {\n 'observation': np.ndarray, # (64, 64, 3) RGB image of current view\n 'desired_goal': np.ndarray, # (64, 64, 3) RGB image of goal location\n 'achieved_goal': np.ndarray, # (64, 64, 3) RGB image of current state\n}\n```\n\n### Reward Structure\n\n- **Goal reaching**: Positive reward when agent reaches the goal location\n- **Step penalty**: Small negative reward per step to encourage efficiency\n- **Episode termination**: When goal is reached or maximum steps exceeded\n\n\n## License\n\nMIT License - see LICENSE file for details.",
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