def __init__(self, game_time, penalty_score):
AbstractPlayer.__init__(
self, game_time, penalty_score
) # keep the inheritance of the parent's (AbstractPlayer) __init__()
self.board = None
self.pos = None
self.minimax = SearchAlgos.MiniMax(utility=self.utility,
succ=self.succ,
perform_move=None,
goal=self.goal,
heuristic=self.heuristic)
self.players_score = None
def __init__(self, game_time, penalty_score):
AbstractPlayer.__init__(
self, game_time, penalty_score
) # keep the inheritance of the parent's (AbstractPlayer) __init__()
# TODO: initialize more fields, if needed, and the Minimax algorithm from SearchAlgos.py
self.board = None # player1 is my player
self.pos_players = None # tuple [0]-player1 pos, [1] -player2 pos
self.strategy = sa.MiniMax(utility=self.utility,
succ=utils.State.succ,
retriveLast=utils.State.retriveLast,
goal=utils.State.goal)
self.num_of_turns = (0, 0
) # tuple [0]-player1 turns, [1] -player2 turns
self.fruits_on_board = None
self.little_edge = 0
def make_move(self, time_limit, players_score):
"""Make move with this Player.
input:
- time_limit: float, time limit for a single turn.
output:
- direction: tuple, specifing the Player's movement, chosen from self.directions
"""
def find_player_positions(player_index):
if player_index == 1:
return self.pos
pos = np.where(self.board == player_index)
# convert pos to tuple of ints
return tuple(ax[0] for ax in pos)
start_time = time()
player_positions = (find_player_positions(1), find_player_positions(2))
self.board[self.pos] = -1
curr_state = SearchAlgos.State(self.board.copy(), tuple(players_score),
player_positions, 0, self.penalty_score,
(0, 0), self.lifetime, self.initial_pos)
minimax = SearchAlgos.MiniMax(minimax_utility, minimax_succ, None,
start_time, time_limit,
minimax_heuristic)
depth = 1
value, direction = minimax.search(curr_state, depth, 1)
while not minimax.developed_whole_tree:
try:
minimax.developed_whole_tree = True
value, direction = minimax.search(curr_state, depth, 1)
depth += 1
except SearchAlgos.Interrupted:
break
i = self.pos[0] + direction[0]
j = self.pos[1] + direction[1]
self.pos = (i, j)
self.board[self.pos] = 1
self.lifetime += 1
return direction
def make_move(self, time_limit, players_score):
"""Make move with this Player.
input:
- time_limit: float, time limit for a single turn.
output:
- direction: tuple, specifing the Player's movement, chosen from self.directions
"""
# print("start computing minimax move") # printing for self test
if self.first_player == -1:
self.first_player = True
state = utils.State(copy.deepcopy(self.board), self.pos,
self.rival_pos, players_score, self.penalty_score,
self.turns_till_fruit_gone,
self.fruits_on_board_dict, self.first_player,
time.time() + time_limit - .015)
search_algo = SearchAlgos.MiniMax(utils.utility, utils.succ,
utils.perform_move, utils.goal)
depth = 1
best_move = None, None
while depth <= self.max_turns:
try:
best_move = search_algo.search(state, depth, True)
except TimeoutError:
break
depth += 1
if best_move[1] is None:
# print("something went wrong,.. im out") # printing for self test
exit(0)
# print("depth is : ", depth-1) # printing for self test
# print("minmax choose the move: ", best_move) # printing for self test
self.board[self.pos] = -1
tmp1 = best_move[1]
self.pos = (self.pos[0] + tmp1[0], self.pos[1] + tmp1[1])
self.board[self.pos] = 1
self.turns_till_fruit_gone -= 1
return best_move[1]
def __init__(self, game_time, penalty_score):
AbstractPlayer.__init__(self, game_time, penalty_score) # keep the inheritance of the parent's (AbstractPlayer) __init__()
self.minimax = SearchAlgos.MiniMax(self.utility, self.succ, self.perform_move, self.goal, self.heuristic_function, self.revert_move)
