This repository contains a customizable framework to create maze and gridworld environments with gym-like API. It has modular designs and it allows large flexibility for the users to easily define their own environments such as changing rendering colors, adding more objects, define available actions etc. The motivation of this repository is, as maze or gridworld are used very often in the reinforcement learning community, however, it is still lack of a standardized framework.

The repo will be actively maintained, any comments, feedbacks or improvements are highly welcomed.

Run the following command to install all required dependencies:

pip install -r requirements.txt

Note that it is highly recommanded to use an Miniconda environment:

conda create -n mazelab python=3.7

Run the following commands to install mazelab from source:

git clone https://github.com/zuoxingdong/mazelab.git
cd mazelab
pip install -e .

Installing from source allows to flexibly modify and adapt the code as you pleased, this is very convenient for research purpose which often needs fast prototyping.

Detailed tutorials is coming soon. For now, it is recommended to have a look in examples/ or the source code.

We have provided a Jupyter Notebook for each example to illustrate how to make various of maze environments, and generate animation of the agent's trajectory following the optimal actions solved by our build-in Dijkstra optimal planner.

• Define Generator: You can define your own maze generator, simply generate a two dimensional numpy array consisting of objects labeled by integers.

• Subclass BaseMaze: Define your own maze by creating all Object and their properties

    class Maze(BaseMaze):
        @property
        def size(self):
            return 10, 10

        def make_objects(self):
            free = Object('free', 0, color.free, False, np.stack(np.where(x == 0), axis=1))
            obstacle = Object('obstacle', 1, color.obstacle, True, np.stack(np.where(x == 1), axis=1))
            agent = Object('agent', 2, color.agent, False, [])
            goal = Object('goal', 3, color.goal, False, [])
            return free, obstacle, agent, goal

• Define Motion: Define your own motion or simply use the one we provided: VonNeumannMotion, MooreMotion

• Define gym environment: Subclass the BaseEnv to define the gym environment.

• Register environment: It is recommended to register your own environment to allow easy-to-use gym.make

    gym.envs.register(id=some_name, entry_point=your_file_or_class, max_episode_steps=some_steps)

• Build-in Dijkstra solver: For simple goal-reaching environment, one could use the build-in Dijkstra solver to compute the optimal action sequences given the current agent position and goal position.

    actions = dijkstra_solver(impassable_array, motions, start, goal)

• Record video of executing optimal actions: Wrap the environment with gym's Monitor to make a video animation.
  • Practical tip: One can use imageio to convert mp4 video to GIF. Refer to the examples for more details.

• 2019-04-05 (v0.2.0)

  • Entirely re-written for much simpler API
  • Subclassing gym.Env to allow all related compatibilities

• 2019-01-01 (v0.1.0)

  • Bugfixes

• 2018-11-18 (v0.0.1)

  • Initial release

• More extensive documentations
• More different kinds of mazes
• More color patterns

Please use this bibtex if you want to cite this repository in your publications:

@misc{mazelab,
      author = {Zuo, Xingdong},
      title = {mazelab: A customizable framework to create maze and gridworld environments.},
      year = {2018},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/zuoxingdong/mazelab}},
    }