def generate_supervised_training_data(cls, games, labeling_strategy):
"""
Generates training data by applying random moves to a board and labeling each sample with the move that :param labeling_strategy would have taken given the board.
:param games: The number of games to be simulated
:param labeling_strategy: The strategy used to label each sample. The label equals labeling_strategy.get_move(board)
:return: a list of tuples(board_sample, move_label)
"""
labeling_strategy.color = cls.config.BLACK
generator = RandomPlayer()
color_iterator = TicTacToeBaseExperiment.AlternatingColorIterator()
start = datetime.now()
training_set = []
for game in range(games):
board = TicTacToeBoard()
for i in range(9):
# generate training pair
expert_move = labeling_strategy.get_move(board)
training_set.append((board.copy(), expert_move))
# prepare for next sample
move = generator.get_move(board)
board.apply_move(move, color_iterator.__next__())
print("Generated %s training pairs form %s games in %s" % (len(training_set), games, datetime.now() - start))
return training_set
def test_DummyUpdate(self):
board = TicTacToeBoard()
value_function = PGStrategy(lr=0.001, weight_decay=0.003)
value_function.evaluate(board.board, board.get_legal_moves_map(config.BLACK))
move = RandomPlayer.get_move(board)
board.apply_move(move, config.BLACK)
value_function.evaluate(board.board, board.get_legal_moves_map(config.BLACK))
move = RandomPlayer.get_move(board)
board.apply_move(move, config.WHITE)
value_function.evaluate(board.board, board.get_legal_moves_map(config.BLACK))
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
def run(self, lr, silent=False):
self.player1 = self.pretrained_player if self.pretrained_player else FCBaseLinePlayer(
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 run(self, lr, termination_criterion, silent=False):
self.player = FCReinforcePlayer(lr=lr)
self.player.color = config.BLACK
generator = RandomPlayer()
print("Pretraining %s on legal moves" % self.player.__str__())
losses, rewards = [], []
start = datetime.now()
for game in range(1, self.max_games + 1):
loss, reward = self.__run_episode__(generator)
losses.append(loss)
rewards.append(reward)
if not silent:
if Printer.print_episode(game, self.max_games,
datetime.now() - start):
plot_name = "Pretraining %s using %s layers on legal moves\nlr: %s" % (
self.player.__class__.__name__, LAYERS, lr)
plot_info = "%sGames - Final reward: %s \nTime: %s" % (
game, reward, config.time_diff(start))
self.plot_and_save(plot_name, plot_name + "\n" + plot_info)
if (100 * game / self.max_games) % 10 == 0:
self.save_player(
self.player,
"using %s layers pretrained on legal moves for %s games lr: %s"
% (LAYERS, self.max_games, lr))
if game > termination_criterion and sum(rewards[
-termination_criterion:]) / termination_criterion == 1:
print(
"Reached training goal: %s games with only legal moves played -> terminating training."
% termination_criterion)
self.save_player(
self.player,
"using %s layers pretrained on legal moves for %s games lr: %s"
% (LAYERS, self.max_games, lr))
return losses, rewards
print("Reached max training_games (%s) -> terminating training" %
self.max_games)
self.save_player(
self.player,
"using %s layers pretrained on legal moves for %s games lr: %s" %
(LAYERS, self.max_games, lr))
return losses, rewards
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 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 run(self, lr, silent=False):
EVALUATION_GAMES = 10
player = FCReinforcePlayer(lr=lr)
player.color = config.BLACK
expert = ExperiencedPlayer(deterministic=True, block_mid=True)
expert.color = config.BLACK
generator = RandomPlayer()
color_iterator = self.AlternatingColorIterator()
validation_set = self.generate_supervised_training_data(
EVALUATION_GAMES,
ExperiencedPlayer(deterministic=True, block_mid=True))
print("Training ReinforcedPlayer supervised continuously with LR: %s" %
lr)
start = datetime.now()
for game in range(self.games):
rewards = []
board = TicTacToeBoard()
for i in range(9):
expert_move = expert.get_move(board)
player_move = player.get_move(board)
reward = config.LABEL_WIN if expert_move == player_move else config.LABEL_LOSS
rewards.append(reward)
# prepare for next sample
move = generator.get_move(board)
board.apply_move(move, color_iterator.__next__())
average_reward = sum(rewards) / len(rewards)
player.strategy.rewards = rewards
loss = player.strategy.update()
del rewards[:]
self.add_results([("Losses", loss), ("Reward", average_reward)])
if game % self.evaluation_period == 0:
test_rewards = []
for board, expert_move in validation_set:
# Evaluation mode
player.strategy.train, player.strategy.model.training = False, False
strategy_move = player.get_move(board)
player.strategy.train, player.strategy.model.training = True, True
test_reward = config.BLACK if expert_move == strategy_move else config.WHITE
test_rewards.append(test_reward)
average_test_reward = sum(test_rewards) / len(test_rewards)
del test_rewards[:]
self.add_results(("Test reward", average_test_reward))
if not silent:
if Printer.print_episode(game + 1, self.games,
datetime.now() - start):
plot_name = "Supervised Continuous training of %s" % (
player)
plot_info = "%s Games - Final reward: %s \nTime: %s" % (
game + 1, average_reward, config.time_diff(start))
self.plot_and_save(plot_name, plot_name + "\n" + plot_info)
return average_reward