Sure, here is an article on how to make a minesweeper environment in OpenAI Gym:

Creating a Minesweeper Environment in OpenAI Gym

OpenAI Gym is a popular toolkit for developing and comparing reinforcement learning algorithms. It provides a wide range of environments for developing and testing RL agents. However, if you want to create a custom environment, you can do so using the Gym framework. In this article, we will walk through the process of creating a Minesweeper environment in OpenAI Gym.

Before we start, let’s have a brief overview of what Minesweeper is. Minesweeper is a classic single-player puzzle video game. The player is presented with a grid of covered squares. Some of the squares contain mines, and the player’s goal is to uncover all the non-mine squares without detonating any mines. The game provides numeric clues that indicate the number of mines adjacent to a particular square, helping the player to deduce the location of the mines.

Now, let’s get started with creating a Minesweeper environment in OpenAI Gym. We will define a custom class that inherits from the `gym.Env` class and implements the required methods.


import gym

from gym import spaces

import numpy as np

class MinesweeperEnv(gym.Env):

def __init__(self, grid_size=10, num_mines=15):

super(MinesweeperEnv, self).__init__()

self.grid_size = grid_size

self.num_mines = num_mines

self.mine_grid = np.zeros((self.grid_size, self.grid_size), dtype=int)

self.action_space = spaces.Discrete(self.grid_size * self.grid_size)

self.observation_space = spaces.Box(low=0, high=8, shape=(self.grid_size, self.grid_size),

def reset(self):

# Reset the environment state

# Initialize the mine grid and return the initial observation


def step(self, action):

# Perform the action in the environment

# Update the state based on the action

# Return the observation, reward, done, and info

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def render(self, mode=’human’):

# Render the current state of the environment



In the `MinesweeperEnv` class, we define an initializer to set up the grid size, number of mines, action space, and observation space. The `reset` method is responsible for initializing the mine grid and returning the initial observation. The `step` method will execute the action and return the updated observation, reward, termination signal (done), and information. Finally, the `render` method will display the current state of the environment.

Now, let’s fill in the methods with the appropriate logic to create a functional Minesweeper environment. We need to implement the mine grid initialization, uncovering logic, and rendering functionality.


# Inside the MinesweeperEnv class

def reset(self):

self.mine_grid = self._initialize_mine_grid()

return self.mine_grid

def step(self, action):

row, col = divmod(action, self.grid_size)

observation = self.mine_grid

reward, done, info = self._uncover_square(row, col)

return observation, reward, done, info

def render(self, mode=’human’):



In the `reset` method, we initialize the mine grid using the `_initialize_mine_grid` method. In the `step` method, we process the action as a row and column index and return the updated observation after uncovering the square using the `_uncover_square` method. The `render` method displays the current state of the mine grid.

We also need to include the helper methods `_initialize_mine_grid` and `_uncover_square` to initialize the mine grid and handle square uncovering logic, respectively.

After implementing these methods, our custom Minesweeper environment in OpenAI Gym should be fully functional. You can then utilize this environment to train and evaluate reinforcement learning agents using popular algorithms such as DQN, A3C, or PPO.

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In conclusion, creating custom environments in OpenAI Gym allows for the development and evaluation of reinforcement learning algorithms tailored to specific problem domains. By following the steps outlined in this article, you can create a Minesweeper environment in OpenAI Gym and use it to explore and develop RL techniques for solving this classic puzzle game.