def __init__(self, logger: Callable = noop_print, random_seed: int = 54):
self.winners = []
self.logger = logger
self.agent = SarsaAgent(1)
self.opponent = SarsaAgent(-1)
self.game = TicTacToeGame()
self.env = Environment(self.game, self.opponent)
self._random = random.Random(random_seed)
class TestBed(object):
def __init__(self, logger: Callable = noop_print, random_seed: int = 54):
self.winners = []
self.logger = logger
self.agent = SarsaAgent(1)
self.opponent = SarsaAgent(-1)
self.game = TicTacToeGame()
self.env = Environment(self.game, self.opponent)
self._random = random.Random(random_seed)
def play_episodes(self, n=100):
print('Episode: ')
for i in range(n):
if i % 100 == 0:
print(f'{i+1}', end=' ')
winner = self.play_episode()
self.winners.append(winner)
def play_episode(self):
if self._random.random() < 0.5:
# Let the opponent start 50% of the time
self.env.opponent_play()
state = self.env.state_rep()
action = self.agent.act(state)
for turn in range(1, 10): # Should be done in 5
reward = self.env.interact(action)
new_state = self.env.state_rep()
new_action = self.agent.act(new_state,
self.game.available_positions())
self.agent.get_feedback(state, action, reward, new_state,
new_action)
state = new_state
action = new_action
if self.env.is_episode_complete:
break
self.logger(f"Episode complete. {turn} turns taken.", end=' ')
result = self.game.winner()
if result:
self.logger(f"{self.game.repr_marker(result)} won!")
else:
self.logger("It was a draw")
self.logger(self.env, end='\n\n\n\n')
self.reset()
return result
def reset(self):
self.game.reset()
self.agent.reset()
self.opponent.reset()
def test_check_game_over_1(self):
"""Test case for the check_game_over function.
Test for game over with a win.
"""
game = TicTacToeGame()
game.state = [[1, 0, -1], [1, 0, -1], [1, 0, -1]]
game_over, value = game.check_game_over(1)
self.assertEqual(game_over, True)
self.assertEqual(value, 1)
def test_check_game_over_4(self):
"""Test case for the check_game_over function.
Test for game not over.
"""
game = TicTacToeGame()
game.state = [[-1, 0, 0], [0, -1, 0], [0, 1, 0]]
game_over, value = game.check_game_over(-1)
self.assertEqual(game_over, False)
self.assertEqual(value, 0)
CFG.dirichlet_alpha = arguments.dirichlet_alpha
CFG.epsilon = arguments.epsilon
CFG.model_directory = arguments.model_directory
CFG.num_eval_games = arguments.num_eval_games
CFG.eval_win_rate = arguments.eval_win_rate
CFG.load_model = arguments.load_model
CFG.human_play = arguments.human_play
CFG.resnet_blocks = arguments.resnet_blocks
CFG.record_loss = arguments.record_loss
CFG.loss_file = arguments.loss_file
CFG.game = arguments.game
# Initialize the game object with the chosen game.
game = object
if CFG.game == 0:
game = TicTacToeGame()
elif CFG.game == 1:
game = OthelloGame()
elif CFG.game == 2:
game = ConnectFourGame()
elif CFG.game == 3:
game = RubiksCubeGame()
net = NeuralNetworkWrapper(game)
# Initialize the network with the best model.
if CFG.load_model:
file_path = CFG.model_directory + "best_model.meta"
if os.path.exists(file_path):
net.load_model("best_model")
else: