def tournament(_players):
"""Holds a tournament for the players
Arguments:
_players {list} -- list of players
game_count {int} -- number of games to play
Returns:
int -- win rates of each player
"""
assert len(_players) == 4, f'Not enough players for ludo - found: {len(_players)}'
players = [player for player in _players]
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
win_rates = np.zeros(4)
for i in range(args.game_count):
random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
return win_rates / args.game_count
def eval_population_worker(queue: mp.Queue, games_per_chromosome, task_counter_queue: mp.Queue, Opponent):
while True:
population_path = queue.get()
folder_path = os.path.dirname(population_path)
folder_name = os.path.basename(folder_path)
player_name = folder_name.split("_")[0]
Player = get_ga_player(player_name)
population = np.load(population_path)
N = min(len(population), 20)
population_idx = np.random.choice(np.arange(len(population)), N, replace=False)
population = population[population_idx]
save_matrix = np.empty((2, N), np.float)
save_matrix[0] = population_idx
scores = save_matrix[1]
for i, chromosome in enumerate(population):
players = [Player(chromosome)] + [Opponent() for _ in range(3)]
win_count = 0
for k in range(games_per_chromosome):
random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
if isinstance(winner, Player):
win_count += 1
scores[i] = win_count / games_per_chromosome
generation_str = os.path.basename(population_path).split(".")[0]
scores_path = get_score_file_path(folder_path, generation_str, Opponent.name)
assert not os.path.exists(scores_path), "Scores already exists: {}".format(scores_path)
np.save(scores_path, save_matrix)
task_counter_queue.put(('finished', population_path))
def eval_player(player):
player_args = get_player_args(player)
for gen in range(args.gen_count):
player.step()
population = player.get_flat_pop()
winner_pop = reduce_pop(args.Player, population, args.games_per_tournament)
players = [args.Player(winner_pop)]
while len(players) < 4:
players.append(LudoPlayerRandom)
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
win_rates = np.zeros(4)
game_count = 100
for i in range(game_count):
np.random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
win_rate = win_rates[0] / game_count
return [win_rate, player_args, winner_pop]
def tournament(chromosomes):
"""Tournament
Arguments:
chromosomes {np.array} -- chromosomes
player {ga_player} -- GA player (simple, advanced or ANN)
game_count {int} -- game count
Returns:
int -- id of the winner
"""
players = [args.player(chromosome) for chromosome in chromosomes]
while len(players) < 4:
players.append(LudoPlayerRandom)
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
win_rates = np.zeros(4)
for i in range(args.games_per_tournament):
random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
ranked_player_ids = np.argsort(-win_rates)
for id in ranked_player_ids:
if id < len(chromosomes):
return id
def play_tournament(self, chromosome_ids, game_count: int):
"""Play tournament
Arguments:
chromosome_ids {np.array} -- ids of the chromosomes to play
game_count {int} -- number of games
"""
flat_pop = self.get_flat_pop()
chromosomes = flat_pop[chromosome_ids]
players = [self.Player(chromosome) for chromosome in chromosomes]
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
win_rates = np.zeros(4)
# bar = tqdm(range(game_count))
# for _ in bar:
for _ in range(game_count):
random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
# bar.set_description(f'Win rates {np.around(win_rates/np.sum(win_rates)*100, decimals=2)}')
ranked_chromosome_ids = chromosome_ids[np.argsort(-win_rates)]
children = self.recombine(*flat_pop[ranked_chromosome_ids[:2]])
children = [self.mutate(child) for child in children]
children = [self.Player.normalize(child) for child in children]
flat_pop[ranked_chromosome_ids[2:]] = children
self.total_game_count += game_count
self.cur_tournament_count += 1
def tournament(chromosomes, player, game_count=10):
players = [player(chromosome) for chromosome in chromosomes]
while len(players) < 4:
players.append(LudoPlayerRandom)
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
win_rates = np.zeros(4)
for i in range(game_count):
np.random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
ranked_player_ids = np.argsort(-win_rates)
for id in ranked_player_ids:
if id < len(chromosomes):
return id
tournament_player_ids = {}
for i, player in enumerate(players):
tournament_player_ids[player] = i
print(f'{player.name} with id {i}')
f.write(f'{player.name} with id {i}\n')
pred_times_sim = []
pred_times_adv = []
pred_times_full = []
win_rates = np.zeros(4)
bar = tqdm(range(game_count))
for i in bar:
random.shuffle(players)
game = LudoGame(players)
winner = players[game.play_full_game()]
win_rates[tournament_player_ids[winner]] += 1
bar.set_description(
f'Win rates {np.around(win_rates/np.sum(win_rates)*100, decimals=2)}'
)
win_rates = win_rates / game_count
print(
f'Win rates {np.around(win_rates/np.sum(win_rates)*100, decimals=4)}')
f.write(
f'Win rates {np.around(win_rates/np.sum(win_rates)*100, decimals=4)}\n'
)
for player in tournament_player_ids: