def place_fruit(self, pos: Position = None):
if pos is None:
pos = Position.random(self.game_map.n, self.game_map.m)
while pos in self.snake:
pos = Position.random(self.game_map.n, self.game_map.m)
self.game_map.set(pos, -1)
return pos
def test_table(table: Table, n: int):
map = GameMap(n, n)
game = Game(map, [Position.random(n, n)])
# game = Game(map, [Position.random(n, n)])
while True:
print('Current State')
game.print()
current_state = calculate_distances(game)
best_action = take_action(current_state, table)
print(f'Action: {best_action}')
result = game.step(best_action)
print(f'Result of move: {result}')
if result < 0:
print('Game over')
return
sleep(2)
def start_training(game: Game, table: Table,
settings: QLearningSettings) -> Table:
current_episode = 0
highest_score = -1
while current_episode < settings.episodes:
current_state = calculate_distances(game)
best_action = take_action(current_state, table)
if random.random() < settings.epsilon:
random_action = random.randint(0, 3)
best_action = list(Direction)[random_action]
success = update_state(best_action, table, settings, game)
if not success:
score = len(game.snake)
print(f'Score: {score}')
print(f'Epsiode: {current_episode + 1}')
if highest_score < score:
highest_score = score
start = Position.random(game.game_map.n, game.game_map.n)
game = Game(game.game_map, [start])
current_episode += 1
print(f'Highest Score: {highest_score}')
return table
