alphabeta4

class AlphaBetaPlayer(IsolationPlayer):
    """Game-playing agent that chooses a move using iterative deepening minimax
    search with alpha-beta pruning. You must finish and test this player to
    make sure it returns a good move before the search time limit expires.
    """
    def get_move(self, game, time_left):
        """Search for the best move from the available legal moves and return a
        result before the time limit expires.

        Modify the get_move() method from the MinimaxPlayer class to implement
        iterative deepening search instead of fixed-depth search.

        **********************************************************************
        NOTE: If time_left() < 0 when this function returns, the agent will
              forfeit the game due to timeout. You must return _before_ the
              timer reaches 0.
        **********************************************************************

        Parameters
        ----------
        game : `isolation.Board`
            An instance of `isolation.Board` encoding the current state of the
            game (e.g., player locations and blocked cells).

        time_left : callable
            A function that returns the number of milliseconds left in the
            current turn. Returning with any less than 0 ms remaining forfeits
            the game.

        Returns
        -------
        (int, int)
            Board coordinates corresponding to a legal move; may return
            (-1, -1) if there are no available legal moves.
        """
        self.time_left = time_left
        best_move = (-1, -1)

        try:
            for depth in range(1, 200):
                best_move = self.alphabeta(game, depth=depth)
        except SearchTimeout:
            pass

        # Return the best move from the last completed search iteration
        return best_move

    def terminal_test(self, game):
        """ Return True if the game is over for the active player
        and False otherwise.
        """
        if self.time_left() < self.TIMER_THRESHOLD:
            raise SearchTimeout()
        return not bool(game.get_legal_moves())

    def max_value(self, game, depth, alpha=float("-inf"), beta=float("inf")):
        """ Return the value for a loss (-1) if the game is over,
        otherwise return the maximum value over all legal child
        nodes.
        """
        if self.time_left() < self.TIMER_THRESHOLD:
            raise SearchTimeout()
        if depth >= self.search_depth:
            return self.score(game, self)
        depth += 1
        if self.terminal_test(game):
            return self.score()
        v = float("-inf")
        for m in game.get_legal_moves():
            v = max(v, self.min_value(game.forecast_move(m), depth, alpha, beta))
            if v >= beta:
                return v
            alpha = max(alpha, v)
        return v

    def min_value(self, game, depth, alpha=float("-inf"), beta=float("inf")):
        """

        :param game:
        :param depth:
        :param alpha:
        :param beta:
        :return:
        """
        if self.time_left() < self.TIMER_THRESHOLD:
            raise SearchTimeout()
        if depth >= self.search_depth:
            return self.score(game, self)
        depth += 1
        if self.terminal_test(game):
            return self.score()
        v = float("inf")
        for m in game.get_legal_moves():
            v = min(v, self.max_value(game.forecast_move(m), depth, alpha, beta))
            if v >= alpha:
                return v
            beta = max(beta, v)
        return v

    def alphabeta(self, game, depth, alpha=float("-inf"), beta=float("inf")):
        """Implement depth-limited minimax search with alpha-beta pruning as
        described in the lectures.

        This should be a modified version of ALPHA-BETA-SEARCH in the AIMA text
        https://github.com/aimacode/aima-pseudocode/blob/master/md/Alpha-Beta-Search.md

        **********************************************************************
            You MAY add additional methods to this class, or define helper
                 functions to implement the required functionality.
        **********************************************************************

        Parameters
        ----------
        game : isolation.Board
            An instance of the Isolation game `Board` class representing the
            current game state

        depth : int
            Depth is an integer representing the maximum number of plies to
            search in the game tree before aborting

        alpha : float
            Alpha limits the lower bound of search on minimizing layers

        beta : float
            Beta limits the upper bound of search on maximizing layers

        Returns
        -------
        (int, int)
            The board coordinates of the best move found in the current search;
            (-1, -1) if there are no legal moves

        Notes
        -----
            (1) You MUST use the `self.score()` method for board evaluation
                to pass the project tests; you cannot call any other evaluation
                function directly.

            (2) If you use any helper functions (e.g., as shown in the AIMA
                pseudocode) then you must copy the timer check into the top of
                each helper function or else your agent will timeout during
                testing.
        """
        if self.time_left() < self.TIMER_THRESHOLD:
            raise SearchTimeout()

        depth = 1
        best_score = alpha
        best_move = None
        for m in game.get_legal_moves():
            v = self.min_value(game.forecast_move(m), depth, alpha, beta)
            if v > best_score:
                best_score = v
                best_move = m
            alpha = max(alpha, v)
        return best_move

Failed Test: 7. Test functionality of AlphaBetaPlayer.alphabeta()

AssertionError: Cut off search too early. (i.e., a correct implementation of alpha beta search did not prune at the same node as your agent when following the same node expansion order.)
Alpha: -inf
Beta: 1.0
Game tree evaluation order:
[(0, 1)]
[(0, 5)]

Nodes are shown with each layer sorted in the order the nodes were expanded
during search. All nodes in each successive layer are children of the
furthest-right node in the parent layer above it.

Test Case Details:

Heuristic: open_move_score
Depth limit: 2
Initial Board State:
0 1 2 3 4 5 6 7 8
0 | | | | | | | | | |
1 | | | - | | | | | | |
2 | 1 | | - | - | - | - | 2 | | |
3 | | - | - | - | - | - | - | | |
4 | | | - | - | | | | | |
5 | | - | - | - | - | - | - | | |
6 | | | | - | - | - | | | |
7 | | | | | | | | | |
8 | | | | | | | | | |

game._board_state:
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56, 2]

Failed Test: 10. Test that AlphaBetaPlayer successfully plays a full game

Traceback (most recent call last):
TypeError: h1() missing 2 required positional arguments: 'game' and 'player'

During handling of the above exception, another exception occurred:

AssertionError: Your agent raised an error while attempting to play a complete game against another agent. Make sure that your agent can play an entire game -- including selecting initial moves on an empty board.
Exception: h1() missing 2 required positional arguments: 'game' and 'player'