def minimax_player(
self,
state,
depth=2500000,
team=1,
heuristic_parameter=True
): # creates first successors to implement minimax algorithm
new_shape_x = np.asarray(state[1]).shape
player1 = Minimax(n=new_shape_x,
default_team=team,
advance_option=heuristic_parameter)
print('default_team', team, player1.default_team)
if team == -1:
state = player1.convert_board_state(state)
best_move = player1.decision_maker(state, depth)
chosen_succ, utility = best_move
if team == -1:
chosen_succ = player1.convert_board_state(chosen_succ)
return chosen_succ
