def test_minimax(self):
p1 = game_agent.MinimaxPlayer()
p1.search_depth = 1
p2 = game_agent.MinimaxPlayer()
p2.search_depth = 1
# Create a smaller board for testing
self.game = isolation.Board(p1, p2, 9, 9)
self.game._board_state = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 21
]
logging.info(self.game.to_string())
# players take turns moving on the board, so player1 should be next to move
assert (p1 == self.game.active_player)
winner, history, outcome = self.game.play()
logging.info("\nWinner: {}\nOutcome: {}".format(winner, outcome))
logging.info(self.game.to_string())
logging.info("Move history:\n{!s}".format(history))
# Check the first selected move
expectedResults = [[2, 4]]
self.assertIn(history[0], expectedResults)
def test_get_moves1(self):
d = 9
self.player1 = game_agent.MinimaxPlayer(search_depth=2)
self.player2 = game_agent.MinimaxPlayer(search_depth=2)
self.game = isolation.Board(self.player1,
self.player2,
width=d,
height=d)
self.game._board_state = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 42
]
self.game.move_count = len(
[x for x in self.game._board_state[0:-4] if x == 1])
print(self.game.to_string())
moves = game_agent.get_moves(self.game, [(6, 5)])
print(len(moves))
print(moves)
self.assertEqual(len(moves), 6)
moves = game_agent.get_moves(self.game, moves)
print(len(moves))
print(moves)
self.assertEqual(len(moves), 13)
class MinimaxTest(unittest.TestCase):
"""Unit tests for MinimaxTests"""
reload(game_agent)
player1 = game_agent.MinimaxPlayer()
player1.time_left = tl
player2 = game_agent.MinimaxPlayer()
# setup a board with no moves to make sure returns correctly
def trivial_setup(self):
game = isolation.Board(self.player1, self.player2, 2, 2)
game.apply_move((0, 1))
game.apply_move((1, 0))
return game
def setUp(self, pos1=(0, 1), pos2=(1, 0)):
game = isolation.Board(self.player1, self.player2)
game.apply_move(pos1)
game.apply_move(pos2)
return game
# Test that minimax returns when no moves left
def testTrivial(self):
game = self.trivial_setup()
ret = self.player1.minimax(game, 3)
self.assertTrue(ret == (-1, -1), ret)
def testMiniMax(self):
game = self.setUp()
ret = self.player1.minimax(game, 3)
def test_minimax(self):
player1 = game_agent.MinimaxPlayer(name='Player 1')
player2 = game_agent.MinimaxPlayer(name='Player 2')
#player2 = sample_players.GreedyPlayer()
game = isolation.Board(player1, player2)
# place player 1 on the board at row 2, column 3, then place player 2 on
# the board at row 0, column 5; display the resulting board state. Note
# that the .apply_move() method changes the calling object in-place.
game.apply_move((2, 3))
game.apply_move((0, 5))
# players take turns moving on the board, so player1 should be next to move
assert (player1 == game.active_player)
'''
# get a list of the legal moves available to the active player
print(game.get_legal_moves())
# get a successor of the current state by making a copy of the board and
# applying a move. Notice that this does NOT change the calling object
# (unlike .apply_move()).
new_game = game.forecast_move((1, 1))
assert(new_game.to_string() != game.to_string())
print("\nOld state:\n{}".format(game.to_string()))
print("\nNew state:\n{}".format(new_game.to_string()))
'''
# play the remainder of the game automatically -- outcome can be "illegal
# move", "timeout", or "forfeit"
winner, history, outcome = game.play()
print("\nWinner: {}\nOutcome: {}".format(winner, outcome))
print(game.to_string())
print("Move history:\n{!s}".format(history))
def setUp(self):
reload(game_agent)
self.player1 = Agent(
game_agent.MinimaxPlayer(score_fn=open_move_score), "MM_Open")
self.player2 = Agent(game_agent.MinimaxPlayer(score_fn=improved_score),
"MM_Improved")
self.game = isolation.Board(self.player1, self.player2)
self.game.play()
def setUp(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer()
self.player2 = sample_players.GreedyPlayer()
self.player3 = sample_players.RandomPlayer()
self.player4 = game_agent.AlphaBetaPlayer()
self.player5 = game_agent.AlphaBetaPlayer()
self.player6 = game_agent.MinimaxPlayer()
self.game = None
def test_empty(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer()
self.player2 = game_agent.MinimaxPlayer()
game = isolation.Board(self.player1, self.player2)
self.assertEqual(game.get_player_location(self.player1), None)
self.assertEqual(game.get_player_location(self.player2), None)
game.play(time_limit=100)
self.assertNotEqual(game.get_player_location(self.player1), None)
self.assertNotEqual(game.get_player_location(self.player2), None)
def setUp(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer(name="Player 1",
search_depth=1)
self.player2 = game_agent.MinimaxPlayer(name="Player 2",
search_depth=1)
self.game = isolation.Board(self.player1,
self.player2,
width=9,
height=9)
self.game.set_board_state(self.ud_submit_board_state)
def _test_minimax(self):
print("---------- testing min max player ----------")
self.player1 = game_agent.MinimaxPlayer()
self.player2 = game_agent.MinimaxPlayer()
for i in range(75):
move = self.game.active_player.get_move(self.game,
time_left=lambda: 100000)
print(move)
self.game.apply_move(move)
print(self.game.print_board())
def test1(self):
player1 = game_agent.MinimaxPlayer()
player2 = game_agent.MinimaxPlayer()
game = isolation.Board(player1, player2)
game.apply_move((2, 3))
game.apply_move((0, 5))
assert (player1 == game.active_player)
winner, history, outcome = game.play()
def setUp(self):
reload(game_agent)
self.minimax_player1 = game_agent.MinimaxPlayer()
self.minimax_player2 = game_agent.MinimaxPlayer()
self.minimax_game = isolation.Board(self.minimax_player1,
self.minimax_player2)
self.alphabeta_player1 = game_agent.AlphaBetaPlayer()
self.alphabeta_player2 = game_agent.AlphaBetaPlayer()
self.alphabeta_game = isolation.Board(self.alphabeta_player1,
self.alphabeta_player2)
def get_next_move(self, board_size, depth, visited_moves):
player1 = game_agent.MinimaxPlayer(depth)
player2 = game_agent.MinimaxPlayer(depth)
self.game = isolation.Board(player1, player2, board_size, board_size)
for move in visited_moves:
print('Applying move {}'.format(move))
self.game.apply_move(move)
print(self.game.print_board())
next_move = player1.get_move(self.game, lambda: 150)
self.game.apply_move(next_move)
print(self.game.print_board())
return next_move
def reset_boards(self):
self.my_player = game_agent.MinimaxPlayer(3,game_agent.custom_score,10)
self.opponent = sample_players.GreedyPlayer(sample_players.open_move_score)
self.game_board_1 = TestBoard(self.my_player, self.opponent)
self.game_board_2 = TestBoard(self.opponent,self.my_player)
self.my_player = game_agent.MinimaxPlayer(5,game_agent.custom_score,10)
self.game_board_3 = TestBoard(self.my_player, self.opponent)
self.my_player = game_agent.MinimaxPlayer(10,game_agent.custom_score,10)
self.game_board_4 = TestBoard(self.my_player, self.opponent)
self.my_player = game_agent.MinimaxPlayer(3,game_agent.custom_score_2,10)
self.game_board_5 = TestBoard(self.my_player, self.opponent)
self.my_player = game_agent.MinimaxPlayer(3,game_agent.custom_score_3,10)
self.game_board_6 = TestBoard(self.my_player, self.opponent)
def testSecondPlayerShouldWinIn2x3(self):
player1 = ga.MinimaxPlayer(search_depth=1, score_fn=sp.open_move_score)
player2 = ga.MinimaxPlayer(search_depth=1, score_fn=sp.open_move_score)
game = isolation.Board(player1, player2, 2, 3)
winner, moves, _ = game.play(100)
self.assertEqual(winner, player2, "player2 should have won the match")
self.assertEqual(len(moves), 2, "there should have been two moves, due to the clear win")
self.assertEqual(moves[0], [2, 1],
"player1 should have picked the top left corner since \
it's the first node in the tree")
self.assertEqual(moves[1], [0, 0],
"player2 should have picked the opposite corner since \
it's the first node that has a direct win")
def test_get_long_path_potential_score(self):
d = 9
self.player1 = game_agent.MinimaxPlayer(search_depth=2)
self.player2 = game_agent.MinimaxPlayer(search_depth=2)
self.game = isolation.Board(self.player1,
self.player2,
width=d,
height=d)
self.game._board_state = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 42
]
self.game.move_count = len(
[x for x in self.game._board_state[0:-4] if x == 1])
print(self.game.to_string())
start = time.time()
score = game_agent.custom_score_2(self.game, self.player1)
elapsed = time.time() - start
print('generations_weighted_difference {}'.format(score))
print('elapsed {}'.format(elapsed))
start = time.time()
score = game_agent.custom_score_3(self.game, self.player1)
elapsed = time.time() - start
print('get_long_path_potential_score {}'.format(score))
print('elapsed {}'.format(elapsed))
moves = game_agent.get_moves(
self.game, [self.game.get_player_location(self.player1)])
start = time.time()
max, avg = game_agent.max_avg_path(self.game, moves)
elapsed = time.time() - start
print('max_avg_path {} {}'.format(max, avg))
print('elapsed {}'.format(elapsed))
start = time.time()
max_pos = 100
max = game_agent.dfs(self.game,
self.game.get_player_location(self.player1),
max_pos)
elapsed = time.time() - start
print('dfs {} max positions {}'.format(max, max_pos))
print('elapsed {}'.format(elapsed))
def test_minimax_interface(self):
"""Test CustomPlayer.minimax interface with simple input """
h, w = 7, 7 # board size
test_depth = 1
starting_location = (5, 3)
adversary_location = (0, 0) # top left corner
heuristic = lambda g, p: 0. # return 0 everywhere
# create a player agent & a game board
agentUT = game_agent.MinimaxPlayer(test_depth, heuristic)
agentUT.time_left = lambda: 99 # ignore timeout for fixed-depth search
board = isolation.Board(agentUT, 'null_agent', w, h)
# place two "players" on the board at arbitrary (but fixed) locations
board.apply_move(starting_location)
board.apply_move(adversary_location)
for move in board.get_legal_moves():
next_state = board.forecast_move(move)
v, _ = agentUT.minimax_with_score(next_state, test_depth, True)
self.assertTrue(
type(v) == float,
("Minimax function should return a floating " +
"point value approximating the score for the " +
"branch being searched."))
def test_minimax(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer()
self.player2 = sample_players.RandomPlayer()
self.game = isolation.Board(self.player1, self.player2)
time_left = lambda : 10.
print(self.player1.get_move(self.game, time_left))
def test_minimax_depth_5(self):
player = game_agent.MinimaxPlayer()
player.time_left = self.create_clock(1000)
best_move = player.minimax(self.board, 5)
self.assertTrue(
best_move in [(2, 3), (3, 2), (2, 5), (3, 6), (5, 2), (5, 6),
(6, 3), (6, 5)], 'best move: ' + str(best_move))
def test_minimax(self):
self.minimax_player = game_agent.MinimaxPlayer(
score_fn=sample_players.center_score, timeout=float("inf"))
self.greedy_player = sample_players.GreedyPlayer()
self.game = isolation.Board(self.minimax_player, self.greedy_player)
# self.game.play(time_limit=float("inf"))
self.minimax_player.match_boards(self.game, self.game)
def test_minimax_get_move(self):
# TODO: All methods must start with "test_"
game = self.game
player = game_agent.MinimaxPlayer()
player.time_left = 10.
print(player.minimax(game, 3))
self.assertTrue(0 == 0)
def test_minimax_no_moves(self):
self.player1 = game_agent.MinimaxPlayer(1, left_and_uppermost, 0)
self.player2 = "Player 2"
self.game = isolation.Board(self.player1, self.player2, 1, 1)
self.game._board_state[0] = 'X'
move = self.player1.get_move(self.game, always_time_left)
self.assertEqual(move, (-1, -1))
def test_alpha_beta(self):
minimax_player = game_agent.MinimaxPlayer(
score_fn=sample_players.open_move_score)
random_player = sample_players.RandomPlayer()
game = isolation.Board(minimax_player, random_player)
result = game.play(time_limit=TIME_LIMIT)
pdb.set_trace()
def test_minimax(self):
player1 = game_agent.MinimaxPlayer()
player2 = game_agent.AlphaBetaPlayer()
the_board = isolation.Board(player1, player2)
assert (the_board.get_legal_moves()
is not None), "Legal moves is not blank"
def test_minimax(self):
#Set up board
print("Setting up inital board for simple minimax player test....")
self.setUp()
#Put some moves on board to create different start states
# Player 1 move 1
self.game.active_player == self.player1
self.game.apply_move((0, 0))
# Player 2 move 1
self.game.active_player == self.player2
self.game.apply_move((0, 2))
## # Player 1 move 2
## self.game.active_player==self.player1
## self.game.apply_move((2,1))
## # Player 2 move 2
## self.game.active_player==self.player2
## self.game.apply_move((2,0))
#Print inital board
print("\nInital state:\n{}".format(self.game.to_string()))
#Calculate player 1 best next move using MiniMax
best_move = (-1, -1)
MinimaxPlayer = game_agent.MinimaxPlayer()
best_move = MinimaxPlayer.get_move(self.game, self.timeLimit)
print('Best move for Player 1 is', best_move)
def xtest_minimax(self):
self.setUp(5, 5)
game = self.game
minimaxPlayer = game_agent.MinimaxPlayer(1, game_agent.custom_score)
#self.game.apply_move((0,2))
#self.game.apply_move((0,4))
print('##################################################')
print(game.to_string())
print('##################################################')
for i in range(25):
moves = game.get_legal_moves()
if len(moves) == 0:
break
self.print_moves(moves)
print('active player', game.active_player)
print('minimax number of legal moves', len(moves))
move = minimaxPlayer.get_move(game, self.timeLeft)
self.print_move(move)
game.apply_move((move))
print('##################################################')
print(game.to_string())
print('##################################################', i)
if game.is_loser(game.active_player):
print(game.active_player, "has lost the game")
print(game.inactive_player, "has won the game")
def test_single_min_max(self):
width, height = 7, 7
#timeout = float("-inf")
timeout = 10.
self.player1 = game_agent.MinimaxPlayer(
search_depth=3, score_fn=game_agent.custom_score, timeout=timeout)
self.player2 = sample_players.GreedyPlayer(
score_fn=game_agent.custom_score_3)
self.game = isolation.Board(self.player1,
self.player2,
width=width,
height=height)
#self.game.apply_move((random.randint(0, self.game.width-1), random.randint(0, self.game.height-1)))
#self.game.apply_move((random.randint(0, self.game.width-1), random.randint(0, self.game.height-1)))
print(self.game.to_string())
# players take turns moving on the board, so player1 should be next to move
assert (self.player1 == self.game.active_player)
# get a list of the legal moves available to the active player
print(self.game.get_legal_moves())
# play the remainder of the game automatically -- outcome can be "illegal
# move", "timeout", or "forfeit"
winner, history, outcome = self.game.play()
print("\nWinner: {}\nOutcome: {}".format(
"player 1" if winner == self.player1 else "player 2", outcome))
print("\nstate:\n{}".format(self.game.to_string()))
print("\nhistory:\n{}".format(history))
def setUp(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer(2,
sample_players.improved_score)
self.player2 = sample_players.GreedyPlayer()
self.game = isolation.Board(self.player1, self.player2, 9, 9)
self.time_left = lambda: float("inf")
def test_minimax_init(self):
self.setUp_random_vs_minimax()
player = game_agent.MinimaxPlayer(search_depth=10, timeout=5.)
self.assertTrue(player.search_depth == 10)
self.assertTrue(player.TIMER_THRESHOLD == 5.)
self.assertTrue(player.score == game_agent.min_value)
pass
def setUp(self):
reload(game_agent)
self.player1 = game_agent.MinimaxPlayer()
self.player2 = sample_players.GreedyPlayer(
sample_players.improved_score)
self.player3 = game_agent.AlphaBetaPlayer()
# self.game = isolation.Board(self.player1, self.player2,7,7)
self.game = isolation.Board(self.player3, self.player2, 7, 7)
def test_minimax_scores_expected_number_of_nodes(self):
self.player1 = game_agent.MinimaxPlayer(score_fn=open_move_score, search_depth=2)
self.game = isolation.Board(self.player1, self.player2, width=9, height=9)
self.game._board_state = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 32]
with mock.patch.object(self.player1, 'score', wraps=self.player1.score) as score_fn:
self.player1.get_move(self.game, lambda : 15.)
self.assertEqual(score_fn.call_count, 35)
