def play_deep_q_model(level='level-0', model_path='./nn_model_level_0_2k_iter.h5'):
dq_model = DeepQ(level)
dq_model.model = load_model(model_path)
def ai_func(current_game_state):
return dq_model.pick_optimal_action(current_game_state)
game = Game(level, init_screen=True, ai_function=ai_func)
game.run()
def play_q_learning_model(level='level-0', model_path='./q_table.pkl'):
q_model = QLearn()
q_model.q_table = load_pickle(model_path)
def ai_func(current_game_state):
return q_model.pick_optimal_action(current_game_state, printing=False)
game = Game(level, init_screen=True, ai_function=ai_func)
game.run()
def run_with_game_loop(level='level-2', model_path='./nn_model4500.h5'):
dq_model = DeepQ(level)
dq_model.model = load_model(model_path)
def ai_func(current_game_state):
return dq_model.pick_optimal_action(current_game_state)
game = Game(level, init_screen=True, ai_function=ai_func)
game.run()
def run_with_game_loop(level='level-0', model_path='./q_table.pkl'):
q_model = QLearn()
q_model.q_table = load_pickle(model_path)
def ai_func(current_game_state):
return q_model.pick_optimal_action(current_game_state)
game = Game(level, init_screen=True, ai_function=ai_func)
game.run()
def test_setup():
game = Game('level-0', True)
game.run()
from pacman.game import Game
from deepq.ai_example import get_suggested_move
if __name__ == '__main__':
game = Game('level-2')
game.run()