def pieces_threatened(game_state, player):
other = game.other_player(player)
home_b = count_pieces(game_state[player])
pieces = 0
for enemy in game_state[other]:
xy = enemy[1], enemy[2]
for move in tokens.available_moves(other):
if move == "Boom":
continue
for dist in range(enemy[0]):
dist = enemy[0] - dist
xy2 = game.dir_to_xy(xy, move, dist)
temp_game = game.Game(game_state)
if tokens.out_of_board(
xy2) or not temp_game.board.is_cell_empty(xy2):
continue
temp_game.move_token(1, xy, move, dist, other)
temp_game.boom(xy2, other)
home_a = count_pieces(temp_game.get_game_state()[player])
pieces += home_b - home_a
return pieces
def __init__(self, game_, game_state, player):
self.game = game_
self.game_state = game_state
self.player = player
self.other = game.other_player(player)
self.turn = 0
self.tt = {}
self.away_recently_moved = None
self.home_recently_moved = None
self.past_states = []
self.root = None
weights = pd.read_csv("genetic_programming/weights.csv",
sep=",",
header=[0])
data = weights.sample(axis=0, random_state=random.randint(0, 1000000))
self.weight_index = data.iloc[0, 0]
self.weight_score = data.iloc[0, 1]
self.weight_games = data.iloc[0, 2]
self.weights = data.iloc[0, 3:].astype(np.float)
def score(self,
curr_state,
game_state,
decision,
player,
alpha=None,
beta=None,
extend=False):
game_state_str = get_str(game_state)
if game_state_str in self.tt:
home_eval, away_eval = self.tt[game_state_str]
else:
if not game_state[player]:
return float("-inf")
elif not game_state[game.other_player(player)]:
return float("inf")
# Not a quiet node
if extend and features.pieces_threatened(game_state,
self.player) > 0:
return self.quiesce(curr_state, game_state, alpha, beta,
self.player)
home_eval, away_eval = features.eval_function(
self, curr_state, game_state, self.player, self.turn)
self.tt[game_state_str] = (home_eval, away_eval)
if decision == "p":
return home_eval
else:
return home_eval - away_eval
def end(self):
game_state = self.game.get_game_state()
self.agent.update_weights(game_state)
with open("genetic_programming/score.json") as file:
data = json.load(file)
if game_state[self.colour] and not game_state[game.other_player(
self.colour)]:
data[self.colour] += 1
elif not game_state[self.colour] and game_state[game.other_player(
self.colour)]:
data[game.other_player(self.colour)] += 1
else:
data["draw"] += 1
with open("genetic_programming/score.json", 'w') as file:
json.dump(data, file)
def min_dist_to_boom(game_state, player):
from math import ceil
if not (game_state[player] and game_state[game.other_player(player)]):
return 0
minimum = float("inf")
for piece1 in game_state[player]:
x1, y1 = piece1[1], piece1[2]
for piece2 in game_state[game.other_player(player)]:
x2, y2 = piece2[1], piece2[2]
dist = y2 - y1 + x2 - x1
minimum = min(minimum, ceil(dist / piece1[0]))
return minimum
def get_children(self, curr_state, game_state, player, strategy):
children = []
next_states = self.available_states(game_state, player)
for next_strategy, next_state in next_states:
state_score = self.utility(curr_state, next_state, strategy,
player)
children.append((state_score, (next_strategy, next_state)))
ordered_children = self.reorder_nodes(children)
children = [
self.Node(x, game.other_player(player), None)
for x in ordered_children
]
return children
def action(self):
"""
This method is called at the beginning of each of your turns to request
a choice of action from your program.
Based on the current state of the game, your player should select and
return an allowed action to play on this turn. The action must be
represented based on the spec's instructions for representing actions.
"""
# TODO: Decide what action to take, and return it
self.past_states.append(self.game_state[self.colour])
self.home_tokens = sum([x[0] for x in self.game_state[self.colour]])
self.away_tokens = sum(
[x[0] for x in self.game_state[game.other_player(self.colour)]])
##################opening book change
action = None
if opening_book.check_early_game():
action = self.opening_book.next_move()
if action:
return action
########################################
if self.away_tokens == 1 and self.home_tokens >= 1:
strategy = self.agent.one_enemy_endgame(self.threshold,
self.max_depth)
elif self.away_tokens == 2 and self.home_tokens >= 2:
strategy = self.agent.two_enemy_endgame(self.threshold,
self.max_depth)
else:
strategy, val = self.agent.mp_mix(self.threshold, self.max_depth)
n, xy, move, distance = strategy
if move == "Boom":
return "BOOM", xy
else:
x_a, y_a = xy
x_b, y_b = game.dir_to_xy(xy, move, distance)
return "MOVE", n, (x_a, y_a), (x_b, y_b)
def quiesce(self, curr_state, game_state, alpha, beta, player):
print(alpha, beta)
home_eval, away_eval = features.eval_function(self, curr_state,
game_state, self.player,
self.turn)
stand_pat = (home_eval - away_eval)
if stand_pat >= beta:
return beta
if alpha < stand_pat:
alpha = stand_pat
next_moves = self.available_states(game_state, player)
for move, next_state in next_moves:
score = -self.quiesce(curr_state, next_state, -beta, -alpha,
game.other_player(player))
if score >= beta:
return beta
if score > alpha:
alpha = score
return alpha
def pieces_per_boom(game_state, player):
other = game.other_player(player)
damages = []
away_before = len(game_state[other])
for piece in game_state[player]:
temp_game = game.Game(game_state)
xy = (piece[1], piece[2])
temp_game.boom(xy, player)
temp_game_state = temp_game.get_game_state()
away_after = len(temp_game_state[other])
damage = away_before - away_after
damages.append(damage)
if len(damages) == 0:
return 0
return max(damages) * max(damages)
def eval_function(agent, curr_state, game_state, player, turn):
other = game.other_player(player)
b_home_pieces = curr_state[player]
b_away_pieces = curr_state[other]
a_home_pieces = game_state[player]
a_away_pieces = game_state[other]
home_num = count_pieces(a_home_pieces)
away_num = count_pieces(a_away_pieces)
total_num = home_num + away_num
if total_num == 0:
return 0, 0
home_pieces_diff = count_pieces(b_home_pieces) - home_num
away_pieces_diff = count_pieces(b_away_pieces) - away_num
# Higher differences have more impact on the game
home_pieces_diff = home_pieces_diff * home_pieces_diff
away_pieces_diff = away_pieces_diff * away_pieces_diff
home_stacks = count_stacks(a_home_pieces)
away_stacks = count_stacks(a_away_pieces)
home_stacks = home_stacks * home_stacks
away_stacks = away_stacks * away_stacks
home_min_dist = min_dist_to_boom(game_state, player)
away_min_dist = min_dist_to_boom(game_state, other)
home_threatening = pieces_threatened(game_state, player)
away_threatning = pieces_threatened(game_state, other)
max_damage = pieces_per_boom(game_state, player)
max_losses = pieces_per_boom(game_state, other)
home_board_score = agent.get_board_score(game_state, player)
away_board_score = agent.get_board_score(game_state, other)
weights = agent.weights
home_features = np.array([
home_num - away_num, home_pieces_diff - away_pieces_diff,
turn * (home_stacks - away_stacks), turn * home_min_dist,
max_damage - max_losses, home_threatening - away_threatning,
home_board_score - away_board_score
])
away_features = np.array([
away_num - home_num, away_pieces_diff - home_pieces_diff,
turn * (away_stacks - home_stacks), turn * away_min_dist,
max_losses - max_damage, away_threatning - home_threatening,
away_board_score - home_board_score
])
home_final = np.dot(home_features, weights)
away_final = np.dot(away_features, weights)
return home_final, away_final
def count_all(game_state, player):
home = sum([x[0] for x in game_state[player]])
away = sum([x[0] for x in game_state[game.other_player(player)]])
return home, away