def getNextState(board, player, action_number):
assert action_number >= 0
hive = represent.load_state_with_player(
board, AIEnvironment._player_to_inner_player(player))
try:
(piece, to_cell) = hive.action_from_vector(action_number)
except HiveException as error:
logging.error("HiveException was caught: {}".format(error))
logging.error("action number: {}".format(action_number))
importexport.export_hive(
hive, importexport.saved_game_path("last_error.json"))
raise
# TODO handle pass
try:
hive.action_piece_to(piece, to_cell)
except HiveException as error:
logging.error("HiveException was caught: {}".format(error))
logging.error(
"action number: {}, resulting action: ({}, {})".format(
action_number, piece, to_cell))
logging.error("Hive:\n{}".format(hive))
importexport.export_hive(
hive, importexport.saved_game_path("last_error.json"))
raise
return represent.two_dim_representation(
represent.get_adjacency_state(hive)), player * (-1)
def getGameEnded_original(board, player_num):
hive = represent.load_state_with_player(
board, AIEnvironment._player_to_inner_player(player_num))
status = hive.check_victory()
if status == GameStatus.UNFINISHED:
return 0
if player_num == 1: # Hive.WHITE
return -1 if status == GameStatus.BLACK_WIN else 1
elif player_num == -1: # Hive.BLACK
return -1 if status == GameStatus.WHITE_WIN else 1
else:
raise ValueError('Unexpected game status')
def getGameEnded_simpified(board, player):
inner_player = AIEnvironment._player_to_inner_player(player)
hive = represent.load_state_with_player(board, inner_player)
res = 0
white_queen_pos = hive.locate("wQ1")
if white_queen_pos:
if len(hive.level.occupied_surroundings(white_queen_pos)) > 1:
res = -1 if inner_player == Player.WHITE else 1
black_queen_pos = hive.locate("bQ1")
if black_queen_pos:
if len(hive.level.occupied_surroundings(black_queen_pos)) > 1:
res = -1 if inner_player == Player.BLACK else 1
return res
def getValidMoves(board, player_num) -> List[int]:
hive = represent.load_state_with_player(
board, AIEnvironment._player_to_inner_player(player_num))
return represent.get_all_action_vector(hive)
def getCanonicalForm(two_dim_repr: List[List[int]], player_num):
hive = represent.load_state_with_player(
two_dim_repr, AIEnvironment._player_to_inner_player(player_num))
return represent.two_dim_representation(
represent.canonical_adjacency_state(hive))