def check_terminal(board: np.ndarray,
_last_action: Optional[PlayerAction] = None) -> bool:
''' check if the board is a "terminal" board: a win or a draw'''
board1 = board.copy()
board2 = board.copy()
board1[board1 == PLAYER1] = NO_PLAYER
board1[board1 == PLAYER2] = BoardPiece(1)
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board1, kernel, 1, 0, 0, BoardPiece(0))
if np.any(result == CONNECT_N):
return True
board2[board2 == PLAYER2] = NO_PLAYER
board2[board2 == PLAYER1] = BoardPiece(1)
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board2, kernel, 1, 0, 0, BoardPiece(0))
if np.any(result == CONNECT_N):
return True
if np.count_nonzero(board) == board.shape[0] * board.shape[1]:
return True
return False
def evaluate_position(array_from_board: np.ndarray, player: BoardPiece) -> int:
"""
This function calculates the heuristic value of the node.
Called by evaluate_curr_board.
Arguments:
array_from_board: ndarray that represents specific column/diagonal/row of the board
player: the player whose moves have to be evaluated
Return:
int: sum of the evaluated values for each subarray of array_from_board
"""
# how many sequences of length 4 does the array contain
index = len(array_from_board) - 3
sum_val = 0 # value of the current array
for i in range(index):
tmp = array_from_board[i:i + 4]
# when there are 3 pieces and space in between
if np.count_nonzero(tmp == player) == 3 and np.count_nonzero(
tmp == BoardPiece(0)) == 1:
sum_val += 1000
# when there are 2 pieces and two spaces in between
if np.count_nonzero(tmp == player) == 2 and np.count_nonzero(
tmp == BoardPiece(0)) == 2:
sum_val += 100
# when there is 1 piece and three spaces
if np.count_nonzero(tmp == player) == 1 and np.count_nonzero(
tmp == BoardPiece(0)) == 3:
sum_val += 1
return sum_val
def other_player(player: BoardPiece) -> BoardPiece:
"""
Function returning the opponent of current player
Arguments:
player: current player
Return:
BoardPiece: opponent player
"""
if player == BoardPiece(1):
return BoardPiece(2)
else:
return BoardPiece(1)
def position_value(board: np.ndarray,
player: BoardPiece,
_last_action: Optional[PlayerAction] = None) -> bool:
"""
Returns the heuristic value to the given plaer of a complete board
"""
board1 = board.copy()
board2 = board.copy()
other_player = BoardPiece(player % 2 + 1)
board1[board1 == other_player] = 5
board1[board1 == player] = BoardPiece(1)
board2[board2 == player] = BoardPiece(5)
board2[board2 == other_player] = BoardPiece(1)
value = 0
# scoring central positions
center = board[:, board.shape[1] // 2]
value += (center == player).sum() * 10
value += (center == other_player).sum() * -5
# checking remainin positions
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board1, kernel, 1, 0, 0, BoardPiece(0))
for i in result:
for sum in i:
if sum == CONNECT_N:
value += 200
if sum == CONNECT_N - 1:
value += 50
if sum == CONNECT_N - 2:
value += 10
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board2, kernel, 1, 0, 0, 0)
for i in result:
for sum in i:
if sum == CONNECT_N:
value += -250
if sum == CONNECT_N - 1:
value += -55
if sum == CONNECT_N - 2:
value += -12
return int(value)
def connected_four_convolve(
board: np.ndarray, player: BoardPiece, _last_action: Optional[PlayerAction] = None
) -> bool:
board = board.copy()
other_player = BoardPiece(player % 2 + 1)
board[board == other_player] = NO_PLAYER
board[board == player] = BoardPiece(1)
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board, kernel, 1, 0, 0, BoardPiece(0))
if np.any(result == CONNECT_N):
return True
return False
def opponent(player: BoardPiece) -> BoardPiece:
"""
Returns opponent player to current given player
:param player: BoardPiece
Player for whom opponent is being calculated
:return: BoardPiece
opponent of current player
"""
if player == BoardPiece(1):
oppo = BoardPiece(2)
else:
oppo = BoardPiece(1)
return oppo
def negamax_alpha_beta(board: np.ndarray, player: BoardPiece, depth: int,
alpha: float, beta: float) -> float:
"""
Search game tree using alpha-beta pruning with negamax.
:param board: current board state
:param player: current player
:param depth: max depth to search in game tree
:param alpha: alpha value for pruning
:param beta: beta value for pruning
:return:
"""
# if we're at an end state,
if (depth == 0) or check_game_over(board):
return evaluate_end_state(board, player)
# otherwise loop over child nodes
other_player = BoardPiece(player % 2 + 1)
value = -np.inf
for move in get_valid_moves(board):
value = max(
value, -negamax_alpha_beta(
apply_player_action(board, move, player, copy=True),
other_player, depth - 1, -beta, -alpha))
alpha = max(alpha, value)
if alpha >= beta:
break
# print(f'value:{value}')
# print(f'depth = {depth}; end state = {check_game_over(board)}; player = {player}')
# print(f'move:{move}; max value:{value}')
return value
def negamax(
board: np.ndarray,
player: BoardPiece,
depth: int,
) -> float:
"""
Search game tree using plain negamax.
This is "colorless" negamax -- it assumes the heuristic value is from the perspective of the player its called on
:param board: current board state
:param player: current player
:param depth: max depth to search in game tree
:return:
"""
# if we're at an end state,
if (depth == 0) or check_game_over(board):
return evaluate_end_state(board, player)
# otherwise loop over child nodes
other_player = BoardPiece(player % 2 + 1)
value = -np.inf
for move in get_valid_moves(board):
value = max(
value,
-negamax(apply_player_action(board, move, player, copy=True),
other_player, depth - 1))
# print(f'value:{value}')
# print(f'depth = {depth}; end state = {check_game_over(board)}; player = {player}')
# print(f'move:{move}; max value:{value}')
return value
def alphabeta(board: np.ndarray,
alpha: np.int8,
beta: np.int8,
MaximisingPlayer: bool,
player: BoardPiece,
depth=4):
'''
depth limited minimax with alpha-beta pruning:
returns the value of the given board based on the minimax algorithm with alpha beta pruning
'''
MinPiece = 3 - player.copy()
MaxPiece = player.copy()
terminalboard = check_terminal(board)
if depth == 0 or terminalboard == True:
return position_value(board, player) * (depth + 1)
if MaximisingPlayer:
value = -999
valid_actions = get_player_actions(board)
for action in valid_actions:
child_board = board.copy()
child_board = apply_player_action(child_board, action, MaxPiece)
value = max(
value,
alphabeta(child_board, alpha, beta, False, player, depth - 1))
alpha = max(alpha, value)
if alpha >= beta:
break #β cut-off
return value
else:
value = 999
valid_actions = get_player_actions(board)
for action in valid_actions:
child_board = board.copy()
child_board = apply_player_action(child_board, action, MinPiece)
value = min(
value,
alphabeta(child_board, alpha, beta, True, player, depth - 1))
beta = min(beta, value)
if beta <= alpha:
break #α cut-off
return value
def window_value(window, player: BoardPiece):
"""
:param window: The window in which the heuristic value of the board is calculated
:param player: Current player playing the game of type BoardPiece
:return: heuristic_value: heuristic value of the board position in the given window of type float
"""
heuristic_value = 0
if player == BoardPiece(1):
opp_player = BoardPiece(2)
else:
opp_player = BoardPiece(1)
if window.count(player) == 4:
heuristic_value += 1000
elif window.count(player) == 3 and window.count(BoardPiece(0)) == 1:
heuristic_value += 10
elif window.count(player) == 2 and window.count(BoardPiece(0)) == 2:
heuristic_value += 5
if window.count(opp_player) == 3 and window.count(BoardPiece(0)) == 1:
heuristic_value -= 90
elif window.count(opp_player) == 2 and window.count(
BoardPiece(0)) == 2:
heuristic_value -= 20
return heuristic_value
def test_apply_player_action():
from agents.common import apply_player_action, PlayerAction
board = np.zeros((6, 7), dtype=BoardPiece)
action = PlayerAction(2)
player = BoardPiece(2)
copy = True
ret = apply_player_action(board, action, player, copy)
assert isinstance(ret, np.ndarray)
def test_alpha_beta():
from agents.agent_minimax import alpha_beta
board = np.zeros((6, 7), dtype=BoardPiece)
player = BoardPiece(2)
depth = 5
alpha = -math.inf
beta = math.inf
maximizingPlayer = True
ret = alpha_beta(board, player, depth, alpha, beta, maximizingPlayer)
assert isinstance(ret, tuple())
def check_result(board: np.ndarray,
player: BoardPiece,
_last_action: Optional[PlayerAction] = None) -> bool:
''' check if the board is a "terminal" board: a win or a draw
and assigns a value to each option that can be used by an evaluation function'''
board1 = board.copy()
board2 = board.copy()
MinPiece = 3 - player
MaxPiece = player
board1[board1 == MinPiece] = NO_PLAYER
board1[board1 == MaxPiece] = BoardPiece(1)
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board1, kernel, 1, 0, 0, BoardPiece(0))
if np.any(result == CONNECT_N):
# print(time)
# print(MaxPiece)
# print("won")
return 1 #self wins
board2[board2 == MaxPiece] = NO_PLAYER
board2[board2 == MinPiece] = BoardPiece(1)
for kernel in (col_kernel, row_kernel, dia_l_kernel, dia_r_kernel):
result = _convolve2d(board2, kernel, 1, 0, 0, BoardPiece(0))
if np.any(result == CONNECT_N):
# print(time)
# print(MinPiece)
# print("lost")
return -0.1 #opponent wins
if np.count_nonzero(board) == board.shape[0] * board.shape[1]:
# print(board)
# print("draw")
return 0 #draw
return False
def generate_move_random(
board: np.ndarray, player: BoardPiece, saved_state: Optional[SavedState]
) -> Tuple[PlayerAction, Optional[SavedState]]:
action = PlayerAction(-1)
# Choose a valid, non-full column randomly and return it as `action`
if player == BoardPiece(2):
valid_columns = []
for col in range(COLUMNS):
if board[ROWS - 1][col] == 0:
valid_columns.append(col)
action = PlayerAction(random.sample(valid_columns, 1))
return action, saved_state
def test_random():
from agents.agents_random.random import generate_move_random
board = np.array([[1, 2, 2, 0, 1, 2, 2],
[2, 1, 1, 2, 1, 2, 2],
[2, 2, 1, 1, 1, 2, 2],
[2, 1, 2, 2, 2, 1, 1],
[1, 2, 1, 1, 1, 2, 2],
[1, 1, 2, 1, 2, 1, 2]])
action, saved_state = generate_move_random(board,BoardPiece(1),saved_state=0)
assert isinstance(action,PlayerAction)
assert action == PlayerAction(3) #Taking the empty one
def evaluate_window(window: list, player: BoardPiece) -> float:
"""
Calculates score for heuristic minimax by counting pieces for agent and opponent player
:param window: list
List containing board snippets (windows) of length window_length as defined in heuristic function
[For connect 4, window_length is 4]
:param player: BoardPiece
Current player for whom board window is being calculated
:return: float
Returns float value of window for agent
"""
score = 0
if player == BoardPiece(1):
opponent = BoardPiece(2)
else:
opponent = BoardPiece(1)
if window.count(1) == 4:
score += 100
elif window.count(player) == 3 and window.count(BoardPiece(0)) == 1:
score += 5
elif window.count(player) == 2 and window.count(BoardPiece(0)) == 2:
score += 2
if window.count(opponent) == 3 and window.count(BoardPiece(0)) == 1:
score -= 4
return score
def expand(self, move: PlayerAction):
"""
Expand the child node for a move; creates a new MonteCarloNode associated with the resulting state.
:param move: a valid move for this node's state.
:return: the resulting node
"""
new_board = apply_player_action(board=self.board, action=move, player=self.to_play, copy=True)
new_node = MonteCarloNode( new_board, to_play=BoardPiece(self.to_play % 2 + 1), last_move=move, parent=self)
self.children[move] = new_node
self.expanded_moves.append(move)
self.unexpanded_moves.pop(self.unexpanded_moves.index(move))
return new_node
def negamax_heuristic(board: np.ndarray, player: BoardPiece) -> float:
"""
A heuristic for negamax -- the weighted sum of n-in-a-row for the current board.
:param board: current board
:param player: the player to play
:return: selected move
"""
board = board.copy()
other_player = BoardPiece(player % 2 + 1)
board[board == other_player] = -1
board[board == player] = 1
score = 0
# if a move results in blocking a loss, return it
for n in range(2, CONNECT_N+1):
weight = weights[n-1]
for _, kernel in enumerate(kernels[n]):
result = _convolve2d(board, kernel, 1, 0, 0, BoardPiece(0))
score += weight * np.sum(result == (n - 1))
return score
def test_expand():
tree = MonteCarlo(player)
tree.make_node(initial_state, player)
key = hash(initial_state.tostring()) + hash(player)
root = tree.nodes[key]
for _ in root.unexpanded_moves:
child = tree.expand(root)
assert isinstance(child, MonteCarloNode)
assert child.last_move in root.legal_moves
assert child.last_move in root.expanded_moves
assert child.parent == root
assert child.to_play == BoardPiece(player % 2 + 1)
child_key = hash(child.board.tostring()) + hash(child.to_play)
assert tree.nodes[child_key] == child
def low_row_heuristic(board:np.ndarray, player:BoardPiece) -> float:
"""
A dumb heuristic to play the move with lowest open row.
:param board: current board
:param player: the player to play
:return: selected move
"""
board = board.copy()
xx, yy = np.meshgrid(np.arange(board.shape[0]), np.arange(board.shape[1]))
# xx.T is row
other_player = BoardPiece(player % 2 + 1)
board[board == other_player] = 0
board[board == player] = 1
weights = xx.T[::-1, :]
return float(np.sum(board*weights))
def test_expand_node():
node = copy.deepcopy(initial_node)
for move in node.unexpanded_moves:
child = node.expand(move)
assert isinstance(child, MonteCarloNode)
assert move == child.last_move
assert child.parent == node
assert child.to_play == BoardPiece(node.to_play % 2 + 1)
# test that attributes are all equal for manually-expanded and method-expanded children
same_child = fully_expanded_node.get_child(move)
d1 = vars(child)
d2 = vars(same_child)
for attribute, value in d1.items():
if attribute in ['parent']:
continue
assert np.all(d2[attribute] == value)
def minimax_value(board: np.ndarray, player: BoardPiece, maxing: bool,
depth: int) -> float:
"""
:param board:
:param player:
:param maxing:
:param depth:
:return:
"""
other_player = BoardPiece(player % 2 + 1)
valid_moves = get_valid_moves(board)
value = 0
if depth == 0 or check_game_over(board):
return evaluate_end_state(board, player)
elif maxing is True:
value = -np.inf
for _, move in enumerate(valid_moves):
# print('Maxing')
# print('move:', move)
MMv = minimax_value(board=apply_player_action(board,
move,
player,
copy=True),
player=player,
maxing=False,
depth=depth - 1)
# print('MM value:', MMv)
value = max(value, MMv)
else:
value = np.inf
for _, move in enumerate(valid_moves):
# print('Mining')
# print('move:', move)
MMv = minimax_value(board=apply_player_action(board,
move,
player,
copy=True),
player=player,
maxing=True,
depth=depth - 1)
# print('MM value:', MMv)
value = min(value, MMv)
return value
def simulate(self, node: MonteCarloNode) -> Union[BoardPiece, GameState]:
"""
Simulate a game from a given node -- outcome is either player or GameState.IS_DRAW
:param node:
:return:
"""
current_rollout_state = node.board.copy()
curr_player = node.to_play
while not check_game_over(current_rollout_state):
possible_moves = get_valid_moves(current_rollout_state)
if possible_moves.size > 1:
action = np.random.choice(list(possible_moves))
else:
action = possible_moves
current_rollout_state = apply_player_action(current_rollout_state, action, curr_player, copy=True)
curr_player = BoardPiece(curr_player % 2 + 1)
return evaluate_end_state(current_rollout_state)
def rollout(self, board: np.ndarray, player: BoardPiece) -> BoardPiece:
"""
Recursive call with opponent, determineWin() check for and return win
:param board:
:param player:
:return: player if won, otherwise continue the rollout with recursive call
"""
if self.isTerminal(board, player):
if self.determineWin(board, player): # if win
return player
elif self.determineWin(board, opponent(player)): # if loss
return opponent(player)
else: # if draw
return BoardPiece(0) # None
else: # generate a random move, create a new board state, apply random move
random_move, saved_state = generate_move_random(board, player)
random_board = apply_player_action(board, random_move, player,
True)
return self.rollout(random_board, opponent(
player)) # recursive call, passing in new board & opponent
def evaluate_end_state(board: np.ndarray,
player: BoardPiece,
heuristic=negamax_heuristic) -> float:
"""
:param heuristic:
:param board:
:param player:
:return:
"""
end = check_end_state(board, player)
other_player = BoardPiece(player % 2 + 1)
if end == GameState.IS_WIN: # win state
return np.inf
elif end == GameState.IS_DRAW: # draw state
return 0
# TODO: workaround to exclude checking the end state twice
elif check_end_state(board, other_player) == GameState.IS_WIN:
return -np.inf
else:
return heuristic(board, player)
def alpha_beta_value(board: np.ndarray, player: BoardPiece, maxing: bool,
depth: int, alpha, beta) -> float:
other_player = BoardPiece(player % 2 + 1)
valid_moves = get_valid_moves(board)
if depth == 0 or check_game_over(board):
return evaluate_end_state(board, player)
elif maxing is True:
value = -np.inf
for _, move in enumerate(valid_moves):
ABv = alpha_beta_value(board=apply_player_action(board,
move,
player,
copy=True),
player=player,
maxing=False,
depth=depth - 1,
alpha=alpha,
beta=beta)
value = max(value, ABv)
alpha = max(alpha, value)
if alpha >= beta:
break
return value
else:
value = np.inf
for _, move in enumerate(valid_moves):
ABv = alpha_beta_value(board=apply_player_action(board,
move,
player,
copy=True),
player=player,
maxing=True,
depth=depth - 1,
alpha=alpha,
beta=beta)
value = min(value, ABv)
beta = min(beta, value)
if beta <= alpha:
break
return value
def evaluate_end_state(board: np.ndarray,
player: BoardPiece,
heuristic=negamax_heuristic) -> float:
"""
Return +/- inf for a win, loss for the given player at the board; 0 for a draw; the hueristic value for an ongoing game.
:param heuristic: heuristic function to call on unfinished game boards
:param board: current board state
:param player: current player
:return:
"""
other_player = BoardPiece(player % 2 + 1)
end = check_end_state(board, player)
if end == GameState.IS_WIN: # win state
return np.inf
elif end == GameState.IS_DRAW: # draw state
return 0
# TODO: avoid checking the end state twice
elif check_end_state(board,
other_player) == GameState.IS_WIN: # lose state
return -np.inf
else: # still playing, use heuristic
return heuristic(board, player)
import timeit
import numpy as np
from agents.common import connected_four, connected_four_convolve, connected_four_iter, initialize_game_state, BoardPiece, PlayerAction, NO_PLAYER, CONNECT_N
"""performance evaluation of connected 4 functions"""
board = initialize_game_state()
number = 10**4
res = timeit.timeit("connected_four_iter(board, player)",
setup="connected_four_iter(board, player)",
number=number,
globals=dict(connected_four_iter=connected_four_iter,
board=board,
player=BoardPiece(1)))
print(f"Python iteration-based: {res/number*1e6 : .1f} us per call")
res = timeit.timeit("connected_four_convolve(board, player)",
number=number,
globals=dict(
connected_four_convolve=connected_four_convolve,
board=board,
player=BoardPiece(1)))
print(f"Convolve2d-based: {res/number*1e6 : .1f} us per call")
res = timeit.timeit("connected_four(board, player)",
setup="connected_four(board, player)",
number=number,
import numpy as np
from agents.common import GameState, BoardPiece, PlayerAction
NO_PLAYER = BoardPiece(
0) # board[i, j] == NO_PLAYER where the position is empty
PLAYER1 = BoardPiece(1) # board[i, j] == PLAYER1 where player 1 has a piece
PLAYER2 = BoardPiece(2) # board[i, j] == PLAYER2 where player 2 has a piece
def test_initialize_game_state():
from agents.common import initialize_game_state
ret = initialize_game_state()
assert isinstance(ret, np.ndarray)
assert ret.dtype == BoardPiece
assert ret.shape == (6, 7)
assert np.all(ret == NO_PLAYER)
"""
assert CONDITON , "OutputString"
"""
def test_pretty_print_board():
from agents.common import pretty_print_board, initialize_game_state
board = initialize_game_state()
board_str = pretty_print_board(board)
nlines = 9
assert len(board_str.splitlines()) == nlines
def minimax_action(board: np.ndarray, player: BoardPiece, saved_state: Optional[SavedState]):
"""Minimax agent getting a board and the corresponding player turn and returning
the best non-full column for the player according to the algorithm.
Enter the current state of the board, and performs a top-bottom search on different positions
of the board, so that the most optimal according the heuristics used is found.
Args:
board: Current state of the board
player: Whose turn is it.
saved_state: Pre-computation work
Returns:
action: Best column to use.
saved_state_out: Tree structure
"""
global BOARD_VALUES
tree = minmax_tree() # Weights tree initialization.
other_player = None
if player == BoardPiece(1): # Finding out which player is who.
other_player = BoardPiece(2)
elif player == BoardPiece(2):
other_player = BoardPiece(1)
idx1 = []
start = -1
for i in range(0, 7): # Player plays
cumul1 = 0 # Initialization of the cumulative variable.
old_board = board.copy()
# Optimal way to start: central column.
if sum(sum(old_board[:, :]) == 0) == 7:
start = 10
break
game, board_val = assign_weight(old_board, i, player, BOARD_VALUES)
break_y, cumul1 = eval_heu(cumul1, board_val, i, idx1, game, node_type=np.array([-1]))
if break_y and cumul1 > 10000: # Already a winning position, break the search.
tree.child[i].value = cumul1
break
elif break_y and cumul1 < 10000: # Full column, do not go down its branches.
tree.child[i].value = cumul1
continue
idx2 = []
for j in range(0, 7): # other player plays
old_board1 = old_board.copy()
game, board_val = assign_weight(old_board1, j, other_player, BOARD_VALUES)
break_y, cumul2 = eval_heu(cumul1, board_val, j, idx2, game)
if break_y: # Either a full-column (worst value given) or a win (best one given).
tree.child[i].child[j].value = cumul2
continue
idx3 = []
for k in range(0, 7): # player plays
old_board2 = old_board1.copy()
game, board_val = assign_weight(old_board2, k, player, BOARD_VALUES)
break_y, cumul3 = eval_heu(cumul2, board_val, k, idx3, game, np.array([-1]))
if break_y: # Either a full-column (worst value given) or a win (best one given).
tree.child[i].child[j].child[k].value = cumul3
continue
idx4 = []
for v in range(0, 7): # other player plays
old_board3 = old_board2.copy()
game, board_val = assign_weight(old_board3, v, other_player, BOARD_VALUES)
break_y, cumul4 = eval_heu(cumul3, board_val, v, idx4, game)
# Last layers' nodes assigned the top-down cumulative heuristic value.
tree.child[i].child[j].child[k].child[v].value = cumul4
_, val_4 = min_child(tree.child[i].child[j].child[k], idx4)
tree.child[i].child[j].child[k].value = val_4 # Assigning the value to father of minimal node.
_, val_3 = max_child(tree.child[i].child[j], idx3)
tree.child[i].child[j].value = val_3 # Assigning the value to father of maximal node.
_, val_2 = min_child(tree.child[i], idx2)
tree.child[i].value = val_2 # Assigning the value to father of minimal node.
action, tree.value = max_child(tree, idx1)
action = PlayerAction(action) # Action to be taken in the Class PlayerAction
if start == 10: # If it is the 1st movement, 1st action performed is the optimal: column 3.
action = 3
saved_state_out = tree
return action, saved_state_out