def test_Performance(self):
p1 = ttt_players.RandomPlayer()
p2 = ttt_players.RandomPlayer()
simulation = TicTacToe([p1, p2])
N = 15000
start = datetime.now()
simulation.run_simulations(N)
print("Simulating %s random games took %s" %
(N, datetime.now() - start))
def test_BlockMid(self):
player1 = ttt_players.ExperiencedPlayer(block_mid=True)
player2 = ttt_players.ExperiencedPlayer(block_mid=False)
simulation = TicTacToe([player1, player2])
results, losses = simulation.run_simulations(self.TEST_EPISODES)
print("Average Result Experienced(block) vs Experienced(): %s" %
np.mean(results))
class TrainReinforcePlayerVsBest(TicTacToeBaseExperiment):
def __init__(self, games, evaluations, pretrained_player=None):
super(TrainReinforcePlayerVsBest, self).__init__()
self.games = games
self.evaluations = evaluations
self.pretrained_player = pretrained_player.copy(
shared_weights=False) if pretrained_player else None
def reset(self):
self.__init__(games=self.games,
evaluations=self.evaluations,
pretrained_player=self.pretrained_player)
return self
def run(self, lr, silent=False):
self.player1 = self.pretrained_player if self.pretrained_player else FCReinforcePlayer(
lr=lr)
# Player 2 has the same start conditions as Player 1 but does not train
self.player2 = self.player1.copy(shared_weights=False)
self.player2.strategy.train = False
games_per_evaluation = self.games // self.evaluations
self.replacements = []
start_time = datetime.now()
for episode in range(1, self.evaluations + 1):
# train
self.player1.strategy.train, self.player1.strategy.model.training = True, True # training mode
self.simulation = TicTacToe([self.player1, self.player2])
results, losses = self.simulation.run_simulations(
games_per_evaluation)
self.add_results(("Losses", np.mean(losses)))
# evaluate
self.player1.strategy.train, self.player1.strategy.model.training = False, False # eval mode
score, results, overview = evaluate_against_base_players(
self.player1)
self.add_loss(np.mean(losses))
self.add_results(results)
if not silent and Printer.print_episode(
episode * games_per_evaluation, self.games,
datetime.now() - start_time):
self.plot_and_save(
"%s vs BEST" % (self.player1),
"Train %s vs Best version of self\nGames: %s Evaluations: %s\nTime: %s"
% (self.player1, episode * games_per_evaluation,
self.evaluations, config.time_diff(start_time)))
if evaluate_against_each_other(self.player1, self.player2):
self.player2 = self.player1.copy(shared_weights=False)
self.player2.strategy.train, self.player2.strategy.model.training = False, False
self.replacements.append(episode)
print("Best player replaced after episodes: %s" % self.replacements)
self.final_score, self.final_results, self.results_overview = evaluate_against_base_players(
self.player1, silent=False)
return self
def evaluate_against_base_players(player,
evaluation_players=[
RandomPlayer(),
NovicePlayer(),
ExperiencedPlayer(),
ExpertPlayer()
],
silent=True):
"""
Standardized evaluation against base players.
:param player: The player to be evaluated
:param evaluation_players: A list of players against which the player should be evaluated
:param silent: Flag controlling if output is written to console
:return: a tuple (score, results) where score is the average score over all evaluation games (scalar (-1, 1)) and results is a list of
tuples (name, score) where 'score' is the score (-1, 1) achieved when evaluated against the player named 'name'.
"""
# Store original training values
if issubclass(player.__class__, LearningPlayer):
training_values = player.strategy.train, player.strategy.model.training
player.strategy.train, player.strategy.model.training = False, False
results = []
for e_player in evaluation_players:
simulation = TicTacToe([player, e_player])
rewards, losses = simulation.run_simulations(config.EVALUATION_GAMES)
results.append([e_player.__str__(), rewards])
if not silent:
print_results(player, e_player, rewards)
# Restore original training values
if issubclass(player.__class__, LearningPlayer):
player.strategy.train, player.strategy.model.training = training_values
avg_results = [(result[0], np.mean(result[1])) for result in results]
avg_results.insert(
0, ("Total Score", np.mean([
res[1] for res in avg_results
]))) # Insert average overall score as first element of results
results_overview = deepcopy(results)
total = Counter(dict())
for entry in results_overview:
entry[1] = Counter(entry[1])
total += entry[1]
results_overview.insert(
0, ("[Total Score]",
total)) # Insert average overall score as first element of results
if not silent:
print("Overall score: %s" % avg_results[0][1])
return avg_results[0][1], avg_results, results_overview
class TrainACPlayerVsTraditionalOpponent(TicTacToeBaseExperiment):
def __init__(self, games, evaluations, pretrained_player, opponent):
super(TrainACPlayerVsTraditionalOpponent, self).__init__()
self.games = games
self.evaluations = evaluations
self.pretrained_player = pretrained_player.copy(shared_weights=False) if pretrained_player else None
self.opponent = opponent
self.milestones = []
def reset(self):
self.__init__(games=self.games, evaluations=self.evaluations, pretrained_player=self.pretrained_player, opponent=self.opponent)
return self
def run(self, lr, silent=False):
self.player1 = self.pretrained_player if self.pretrained_player else FCACPlayer(lr=lr)
if self.opponent is not None:
self.player2 = self.opponent
self.simulation = TicTacToe([self.player1, self.player2])
games_per_evaluation = self.games // self.evaluations
start_time = datetime.now()
for episode in range(1, self.evaluations+1):
if self.opponent is None:
self.player2 = choice((RandomPlayer(), NovicePlayer(), ExperiencedPlayer())) # choice((RandomPlayer(), ExpertPlayer()))
self.simulation = TicTacToe([self.player1, self.player2])
# train
self.player1.strategy.train, self.player1.strategy.model.training = True, True # training mode
results, losses = self.simulation.run_simulations(games_per_evaluation)
self.add_loss(np.mean(losses))
self.add_results(("Training Results", np.mean(results)))
# evaluate
self.player1.strategy.train, self.player1.strategy.model.training = False, False # eval mode
if self.opponent is None:
score, results, overview = evaluate_against_base_players(self.player1)
else:
score, results, overview = evaluate_against_base_players(self.player1, evaluation_players=[self.opponent])
self.add_results(results)
if not silent:
if Printer.print_episode(episode*games_per_evaluation, self.games, datetime.now() - start_time):
overview = format_overview(overview)
self.plot_and_save(
"%s vs TRADITIONAL OPPONENT" % (self.player1),
"Train %s vs %s\nGames: %s Evaluations: %s\nTime: %s"
% (self.player1, self.opponent, episode * games_per_evaluation, self.evaluations, config.time_diff(start_time)))
self.final_score, self.final_results, self.results_overview = evaluate_against_base_players(self.player1, silent=False)
return self
def test_neverLose(self):
GAMES = 10000
player1 = SearchPlayer()
player2 = SearchPlayer()
random_player = RandomPlayer()
simulation = TicTacToe([player1, player2])
results, losses = simulation.run_simulations(GAMES // 100)
self.assertEqual(
len(results), results.count(0),
"Perfect player mirror match resulted in a result other than draw")
print("\nFirst 20 results: %s against self" % results[:20])
simulation = TicTacToe([player1, random_player])
results, losses = simulation.run_simulations(GAMES)
self.assertEqual(0, results.count(-1),
"Perfect player lost against random")
print("First 20 results: %s against random player" % results[:20])
print("Win rate: %s vs random player" % (sum(results) / len(results)))
def testRandomPlayer(self):
player1 = ttt_players.RandomPlayer()
player2 = ttt_players.RandomPlayer()
simulation = TicTacToe([player1, player2])
results, losses = simulation.run_simulations(self.TEST_EPISODES)
self.assertTrue(len(results) == self.TEST_EPISODES)
self.assertTrue(None not in results)
for i in range(4):
simulation.__run__(player1, player2)
black_stones, white_stones = simulation.board.count_stones()
self.assertIn(black_stones,
[white_stones - 1, white_stones, white_stones + 1])
if not simulation.board.game_won():
self.assertEqual(black_stones + white_stones,
simulation.board.board_size**2)
print("Average Result Random vs Random: %s" % np.mean(results))
class TrainBaselinePlayerVsSelf(TicTacToeBaseExperiment):
def __init__(self, games, evaluations, pretrained_player=None):
super(TrainBaselinePlayerVsSelf, self).__init__()
self.games = games
self.evaluations = evaluations
self.pretrained_player = pretrained_player.copy(
shared_weights=False) if pretrained_player else None
self.milestones = []
def reset(self):
self.__init__(games=self.games,
evaluations=self.evaluations,
pretrained_player=self.pretrained_player)
return self
def run(self, lr, milestones=False, silent=False):
self.player1 = self.pretrained_player if self.pretrained_player else FCBaseLinePlayer(
lr=lr)
# Player 2 has the same start conditions as Player 1 but does not train
self.player2 = self.player1.copy(shared_weights=True)
self.player2.strategy.train = False
self.simulation = TicTacToe([
self.player1, self.player2
]) # Players never change, therefore we only need one simulation
games_per_evaluation = self.games // self.evaluations
start_time = datetime.now()
for episode in range(1, self.evaluations + 1):
# train
self.player1.strategy.train, self.player1.strategy.model.training = True, True # training mode
# If milestones exist, use them with probability p
if self.milestones and random() < 0.2:
self.player2 = choice(self.milestones)
else:
self.player2 = self.player1.copy(shared_weights=True)
# train
self.player1.strategy.train, self.player1.strategy.model.training = True, True # training mode
self.player2.strategy.train = False
results, losses = self.simulation.run_simulations(
games_per_evaluation)
self.add_loss(np.mean(losses))
self.add_results(("Self", np.mean(results)))
# evaluate
self.player1.strategy.train, self.player1.strategy.model.training = False, False # eval mode
score, results, overview = evaluate_against_base_players(
self.player1)
self.add_results(results)
if not silent and Printer.print_episode(
episode * games_per_evaluation, self.games,
datetime.now() - start_time):
self.plot_and_save(
"%s vs SELF" % (self.player1.__str__() +
(" milestones" if milestones else "")),
"Train %s vs Self version of self\nGames: %s Evaluations: %s\nTime: %s"
% (self.player1, episode * games_per_evaluation,
self.evaluations, config.time_diff(start_time)))
# If x/5th of training is completed, save milestone
if milestones and (self.games / episode *
games_per_evaluation) % 5 == 0:
self.milestones.append(self.player1.copy(shared_weights=False))
self.milestones[-1].strategy.train = False
self.final_score, self.final_results, self.results_overview = evaluate_against_base_players(
self.player1, silent=False)
return self
def test_ConvReinforcePlayer(self):
fc_player = FCReinforcePlayer(lr=1e-4)
random_player = RandomPlayer()
simulation = TicTacToe([fc_player, random_player])
simulation.run_simulations(100)
def test_DummyTrainReinforcePlayer(self):
player1 = FCReinforcePlayer(lr=0.001)
player2 = RandomPlayer()
simulation = TicTacToe([player1, player2])
simulation.run_simulations(10)
def test_ExperiencedVsRandom(self):
player1 = ttt_players.ExperiencedPlayer()
player2 = ttt_players.RandomPlayer()
simulation = TicTacToe([player1, player2])
results, losses = simulation.run_simulations(self.TEST_EPISODES)
print("Average Result Experienced vs Random: %s" % np.mean(results))