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https://github.com/MattChanTK/gym-maze
A basic 2D maze environment where an agent start from the top left corner and try to find its way to the bottom left corner.
https://github.com/MattChanTK/gym-maze
Last synced: 29 days ago
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A basic 2D maze environment where an agent start from the top left corner and try to find its way to the bottom left corner.
- Host: GitHub
- URL: https://github.com/MattChanTK/gym-maze
- Owner: MattChanTK
- Created: 2016-10-05T02:52:08.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-10-09T08:17:27.000Z (about 1 year ago)
- Last Synced: 2024-08-04T02:09:03.748Z (4 months ago)
- Language: Python
- Size: 27.3 KB
- Stars: 348
- Watchers: 7
- Forks: 107
- Open Issues: 14
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-model-based-RL - 2D maze navigation - miniworld](https://github.com/maximecb/gym-miniworld) (Papers / NeurIPS 2021)
README
# gym-maze
A simple 2D maze environment where an agent (blue dot) finds its way from the top left corner (blue square) to the goal at the bottom right corner (red square).
The objective is to find the shortest path from the start to the goal.
### Action space
The agent may only choose to go up, down, right, or left ("N", "S", "E", "W"). If the way is blocked, it will remain at the same the location.
### Observation space
The observation space is the (x, y) coordinate of the agent. The top left cell is (0, 0).
### Reward
A reward of 1 is given when the agent reaches the goal. For every step in the maze, the agent recieves a reward of -0.1/(number of cells).
### End condition
The maze is reset when the agent reaches the goal.
## Maze Versions
### Pre-generated mazes
* 3 cells x 3 cells: _MazeEnvSample3x3_
* 5 cells x 5 cells: _MazeEnvSample5x5_
* 10 cells x 10 cells: _MazeEnvSample10x10_
* 100 cells x 100 cells: _MazeEnvSample100x100_
### Randomly generated mazes (same maze every epoch)
* 3 cells x 3 cells: _MazeEnvRandom3x3_
* 5 cells x 5 cells: _MazeEnvRandom5x5_
* 10 cells x 10 cells: _MazeEnvRandom10x10_
* 100 cells x 100 cells: _MazeEnvRandom100x100_
### Randomly generated mazes with portals and loops
With loops, it means that there will be more than one possible path.
The agent can also teleport from a portal to another portal of the same colour.
* 10 cells x 10 cells: _MazeEnvRandom10x10Plus_
* 20 cells x 20 cells: _MazeEnvRandom20x20Plus_
* 30 cells x 30 cells: _MazeEnvRandom30x30Plus_
## Installation
It should work on both Python 2.7+ and 3.4+. It requires pygame and numpy.
```bash
cd gym-maze
python setup.py install
```
## Examples
An example of finding the shortest path through the maze using Q-learning can be found here: https://github.com/tuzzer/ai-gym/blob/master/maze_2d/maze_2d_q_learning.py
