def main():
board_size = 4
max_num_moves = int(board_size ** 2)
state_space_size = int(board_size ** 2 + 1)
conv_layers = []
state_space_size = 128
hidden_layers = [state_space_size, max_num_moves]
la = 0.01
state_manager = StateManager(board_size)
num_simulations = 200
player1 = NeuralActor(conv_layers, hidden_layers, max_num_moves, la, 'sgd')
player2 = NeuralActor(conv_layers, hidden_layers, max_num_moves, la, 'sgd')
mct1 = MCT(player1, num_simulations)
mct2 = MCT(player2, num_simulations)
train = False
if train == True:
start_time = time.time()
for i in range(0, 100):
mct1.play_game(copy.deepcopy(state_manager))
training_data = mct1.get_training_data()
loss = player1.update_Q(training_data)
print(str(i) + " " + str(loss))
player1.store_model('data/16.3')
else:
player1.load_model('data/16.3')
player2.load_model('data/16.3')
win1 = 0
win2 = 0
for i in range(0, 1000):
state_manager = StateManager(board_size)
while not state_manager.player1_won() and not state_manager.player2_won():
if not state_manager.player1_to_move:
move_index = random.randrange(0, board_size ** 2)
while not StateManager.is_legal(move_index, state_manager.string_representation()):
move_index = random.randrange(0, board_size ** 2)
move = state_manager.convert_to_move(move_index)
move = state_manager.convert_to_move(player2.get_action(state_manager.string_representation()))
else:
move_index = random.randrange(0, board_size ** 2)
while not StateManager.is_legal(move_index, state_manager.string_representation()):
move_index = random.randrange(0, board_size ** 2)
move = state_manager.convert_to_move(move_index)
#move = state_manager.convert_to_move(player2.get_action(state_manager.string_representation()))
state_manager.make_move(move)
#state_manager.show()
if state_manager.player1_won():
win1 += 1
elif state_manager.player2_won():
win2 += 1
else:
print("No winner")
print("Times player 1 won: " + str(win1) + ". " + "Times player2 won: " + str(win2))
def expand(self):
size = int((len(self.state) - 1)**0.5)
state_manager = StateManager(size, self.state)
possible_moves = state_manager.get_moves()
self.children = []
for move in possible_moves:
state_manager.make_move(move)
self.children.append((Node(self,
state_manager.string_representation()),
0)) # Visited 0 times
state_manager.undo_move(move)
def rollout(self, leaf):
size = int((len(leaf.state) - 1) ** 0.5)
leaf_state, first_iteration = StateManager(size, leaf.state), True
while True:
if leaf_state.player1_won():
score = 1.0
return score
elif leaf_state.player2_won():
score = -1.0
return score
if leaf_state.is_finished():
print("No winner error")
quit()
if first_iteration:
possible_moves = leaf_state.get_moves()
move = possible_moves[random.randint(0, len(possible_moves) - 1)]
first_iteration = False
else:
move = leaf_state.convert_to_move(self.nn.get_action(leaf_state.string_representation()))
leaf_state.make_move(move)