Exemple #1
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 def test_model(self, model):
     steps_arr = []
     scores_arr = []
     for _ in range(self.test_games):
         steps = 0
         game_memory = []
         game = Game()
         _, score, snake, apple = game.start()
         prev_observation = self.generate_observation(snake, apple)
         for _ in range(self.goal_steps):
             predictions = []
             for action in range(-1, 2):
                 predictions.append(
                     model.predict(
                         self.add_action_to_observation(
                             prev_observation, action).reshape(-1, 5, 1)))
             action = np.argmax(np.array(predictions))
             game_action = self.get_game_action(snake, action - 1)
             done, score, snake, apple = game.step(game_action)
             game_memory.append([prev_observation, action])
             if done:
                 print('-----')
                 print(steps)
                 print(snake)
                 print(apple)
                 print(prev_observation)
                 print(predictions)
                 break
             else:
                 prev_observation = self.generate_observation(snake, apple)
                 steps += 1
         steps_arr.append(steps)
         scores_arr.append(score)
     print('Average steps:', mean(steps_arr))
     print(Counter(steps_arr))
     print('Average score:', mean(scores_arr))
     print(Counter(scores_arr))
Exemple #2
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    def initial_population(self):
        training_data = []
        for _ in range(self.initial_games):
            display = pygame.display.set_mode(
                (Config['game']['width'], Config['game']['height']))
            pygame.display.set_caption(Config['game']['caption'])

            game = Game(display)

            _, prev_score, snake, apple = game.start()
            prev_observation = self.generate_observation(snake, apple)
            prev_apple_distance = self.get_apple_distance(snake, apple)
            for _ in range(self.goal_steps):
                action, game_action = self.generate_action(snake)
                done, score, snake, apple = game.step(game_action)
                if done:
                    training_data.append([
                        self.add_action_to_observation(prev_observation,
                                                       action), -1
                    ])
                    break
                else:
                    apple_distance = self.get_apple_distance(snake, apple)
                    if score > prev_score or apple_distance < prev_apple_distance:
                        training_data.append([
                            self.add_action_to_observation(
                                prev_observation, action), 1
                        ])
                    else:
                        training_data.append([
                            self.add_action_to_observation(
                                prev_observation, action), 0
                        ])
                    prev_observation = self.generate_observation(snake, apple)
                    prev_apple_distance = apple_distance
        return training_data
Exemple #3
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#!/usr/bin/python
# -*- encoding: utf-8 -*-

__author__ = 'Oliver Banse'


# IDEA
# Import and Initialization
import pygame

from classes.Game import Game
from classes.Screen import Screen

pygame.init()

# Display configuration
screen = Screen('Hit the Mole!', 800, 600)
# Entities
game = Game(screen)
# Run Game
game.start()


# if __name__ == '__main__':
#     game.start()
Exemple #4
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 def build(self):
     game = Game()
     game.start()
     timer = Clock.schedule_interval(game.timer, 1)
     return game