Minesweeper Env (#2)

### Changes
* Implements minesweeper gym env
* Adds unit tests
* Changes to using `virtualenv` over `pipenv` due to [locking taking an eternity](pypa/pipenv#3827)

.circleci/config.yml

@@ -3,26 +3,31 @@ version: 2
 jobs:
   build:
     docker:
-      - image: alpine
-        environment:
-          LANG: en_us.UTF-8
-          PIPENV_VENV_IN_PROJECT: true
-          PIPENV_DEV: true
+      - image: archlinux:base-devel
 
     steps:
       - checkout
 
       - run:
           name: Install Required Tools
+
+          # 1. Python deps
+          # 2. python-pillow does not provide wheel, so must be built with these deps
+          # 3. Formatting/linting
           command: |
-            sed -i -e 's/v[[:digit:]]\..*\//edge\//g' /etc/apk/repositories
-            echo "http://dl-cdn.alpinelinux.org/alpine/edge/testing/" >> /etc/apk/repositories
-            apk add --no-cache bash py3-pip shellcheck shfmt
-            pip install pipenv
+            pacman -Sy --noconfirm \
+              python python-pip python-virtualenv \
+              lcms2 libtiff openjpeg2 libimagequant libxcb \
+              shellcheck shfmt
 
       - run:
           name: Set Up Virtualenv
-          command: pipenv install
+          command: |
+            python -m venv venv
+            echo "source venv/bin/activate" >> $BASH_ENV
+            source venv/bin/activate
+            pip install --upgrade pip wheel
+            pip install -e .[dev]
 
       - run:
           name: Format

.gitignore

@@ -1,4 +1,5 @@
 /.idea/
-/Pipfile.lock
 __pycache__
 /test_results/
+/venv/
+*.egg*

gym_minesweeper/__init__.py

@@ -0,0 +1,5 @@
+"""OpenAI gym environment for minesweeper."""
+
+__all__ = ['MinesweeperEnv', 'SPACE_MINE', 'SPACE_UNKNOWN', 'REWARD_WIN', 'REWARD_LOSE', 'REWARD_CLEAR']
+
+from gym_minesweeper.minesweeper import MinesweeperEnv, SPACE_MINE, SPACE_UNKNOWN, REWARD_WIN, REWARD_LOSE, REWARD_CLEAR

gym_minesweeper/minesweeper.py

@@ -0,0 +1,205 @@
+"""OpenAI gym environment for minesweeper."""
+
+import sys
+from io import StringIO
+
+import gym
+import numpy as np
+from gym import spaces
+from gym.utils import seeding
+
+DEFAULT_BOARD_SIZE = (16, 30)
+DEFAULT_NUM_MINES = 99
+
+SPACE_MINE = -2
+SPACE_UNKNOWN = -1
+SPACE_MAX = 8
+
+REWARD_WIN = 1000
+REWARD_LOSE = -100
+REWARD_CLEAR = 5
+
+
+# Based on https://github.com/genyrosk/gym-chess/blob/master/gym_chess/envs/chess.py
+# pylint: disable=R0902
+class MinesweeperEnv(gym.Env):
+    """Minesweeper gym environment."""
+
+    metadata = {"render.modes": ["ansi", "human"]}
+
+    def __init__(self, board_size=DEFAULT_BOARD_SIZE, num_mines=DEFAULT_NUM_MINES):
+        assert np.prod(board_size) >= num_mines
+        assert len(board_size) == 2
+        self.board_size, self.num_mines = board_size, num_mines
+        self.hist, self.board, self._board, self._rng = None, None, None, None
+
+        self.observation_space = spaces.Box(SPACE_MINE, SPACE_MAX + 1, board_size, np.int)
+        self.action_space = spaces.Discrete(np.prod(board_size))
+        self.reset()
+
+    def step(self, action):
+        """Run one timestep of the environment's dynamics. When end of
+        episode is reached, you are responsible for calling `reset()`
+        to reset this environment's state.
+
+        Accepts an action and returns a tuple (observation, reward, done, info).
+
+        Args:
+            action (np.array): [x, y] coordinate pair of space to clear
+
+        Returns:
+            observation (np.array[np.array]): current board state
+            reward (float) : amount of reward returned after previous action
+            done (bool): whether the episode has ended, in which case further step() calls will return undefined results
+            info (dict): currently contains nothing
+        """
+
+        target_x, target_y = tuple(action)
+        assert self._is_clearable_space(target_x, target_y), "Invalid action: {}".format(action)
+
+        # If first step, populate board
+        # We do this here so that the first move never triggers a mine to explode
+        if len(self.hist) == 0:
+            # Place mines in private board
+            mines_placed = 0
+            while mines_placed < self.num_mines:
+                mine_indices = list(
+                    zip(*
+                        [self._rng.randint(0, dim_size, self.num_mines - mines_placed)
+                         for dim_size in self.board_size]))
+                for i in mine_indices:
+                    if self._board[i] == SPACE_UNKNOWN:
+                        # prohibit mines adjacent or equal to target on first step
+                        if i[0] > target_x + 1 or i[0] < target_x - 1 or i[1] > target_y + 1 or i[1] < target_y - 1:
+                            self._board[i] = SPACE_MINE
+                            mines_placed += 1
+
+            # Calculate nearby mines in private board
+            for calc_x in range(self.board_size[0]):
+                for calc_y in range(self.board_size[1]):
+                    if self._board[calc_x, calc_y] == SPACE_UNKNOWN:
+                        self._board[calc_x, calc_y] = self._num_nearby_mines(calc_x, calc_y)
+
+        self._clear_space(target_x, target_y)
+
+        status = self.get_status()
+
+        if status is None:
+            return self.board, 5, False, dict()
+        if status:
+            # if won, no need to reveal mines
+            return self.board, 1000, True, dict()
+        # if lost, reveal mines
+        self.board = self._board
+        return self.board, -100, True, dict()
+
+    def reset(self):
+        """Resets the environment to an initial state and returns an initial
+        observation.
+
+        Note that this function does not reset the environment's random
+        number generator(s); random variables in the environment's state are
+        sampled independently between multiple calls to `reset()`. In other
+        words, each call of `reset()` yields an environment suitable for
+        a new episode, independent of previous episodes.
+
+        Returns:
+            observation (np.array[np.array]): current board state (all unknown)
+        """
+
+        self.hist = []
+        self._board = np.full(self.board_size, SPACE_UNKNOWN, np.int)
+        self.board = np.array(self._board)
+        return self.board
+
+    def render(self, mode='human'):
+        """Renders the environment.
+
+        If mode is:
+
+        - human: render to the current display or terminal and
+          return nothing. Usually for human consumption.
+        - ansi: Return a StringIO.StringIO containing a
+          terminal-style text representation. The text may include newlines
+          and ANSI escape sequences (e.g. for colors).
+
+        Args:
+            mode (str): the mode to render with
+
+        Returns:
+            outfile (StringIO or None): StringIO stream if mode is ansi, otherwise None
+        """
+
+        outfile = StringIO() if mode == 'ansi' else sys.stdout if mode == 'human' else super().render(mode)
+        for i, dim_1 in enumerate(self.board):
+            for j, dim_2 in enumerate(dim_1):
+                if dim_2 == SPACE_MINE:
+                    outfile.write('X')
+                elif dim_2 == SPACE_UNKNOWN:
+                    outfile.write('-')
+                else:
+                    outfile.write(str(dim_2))
+                if j != self.board_size[1] - 1:
+                    outfile.write(' ')
+            if i != self.board_size[0] - 1:
+                outfile.write('\n')
+        if mode == 'ansi':
+            return outfile
+        return None
+
+    def seed(self, seed=None):
+        """Sets the seed for this env's random number generator(s).
+
+        Returns:
+            list<bigint>: Returns the list of seeds used in this env's random
+              number generators. In this case, the length is 1.
+        """
+
+        self._rng, seed = seeding.np_random(seed)
+        return [seed]
+
+    def _is_valid_space(self, target_x, target_y):
+        return 0 <= target_x < self.board_size[0] and 0 <= target_y < self.board_size[1]
+
+    def _is_clearable_space(self, target_x, target_y):
+        return self._is_valid_space(target_x, target_y) and self.board[target_x, target_y] == SPACE_UNKNOWN
+
+    def _num_nearby_mines(self, target_x, target_y):
+        num_mines = 0
+        for i in range(target_x - 1, target_x + 2):
+            for j in range(target_y - 1, target_y + 2):
+                if (target_x != i or target_y != j) and self._is_valid_space(i, j) and self._board[i, j] == SPACE_MINE:
+                    num_mines += 1
+        return num_mines
+
+    def _clear_space(self, target_x, target_y):
+        spaces_to_clear = {(target_x, target_y)}
+        spaces_cleared = set()
+
+        update_hist = True
+        while spaces_to_clear:
+            current_space = next(iter(spaces_to_clear))
+            self.board[current_space[0], current_space[1]] = self._board[current_space[0], current_space[1]]
+            if update_hist:
+                self.hist.append(current_space)
+                update_hist = False
+
+            spaces_to_clear.remove(current_space)
+            spaces_cleared.add(current_space)
+
+            if self.board[current_space[0], current_space[1]] == 0:
+                for i in range(current_space[0] - 1, current_space[0] + 2):
+                    for j in range(current_space[1] - 1, current_space[1] + 2):
+                        if self._is_valid_space(i, j) and (i, j) not in spaces_cleared:
+                            spaces_to_clear.add((i, j))
+
+    def get_status(self):
+        """Gets the status of the game.
+
+        Returns:
+            status (bool): True if game won, False if game lost, None if game in progress
+        """
+
+        if np.count_nonzero(self.board == SPACE_MINE):
+            return False
+        return True if np.count_nonzero(self.board == SPACE_UNKNOWN) == self.num_mines else None