class SnakeEnv(Env):
def __init__(self):
self.action_space = Discrete(3) # 0 = turn left, 1 = do nothing, 2 = turn right
self.state = [0, 0, 1, 0]
self.game = Game()
self.reward = 0
self.done = False
def step(self, action):
offset = (action - 1)
translated_action = offset + self.game.snake.direction
if translated_action < 0:
translated_action = 3
if translated_action > 3:
translated_action = 0
self.reward, self.done = self.game.run(1, translated_action)
diff = (self.game.food.position[0] - self.game.snake.snake[0][0], self.game.food.position[1] - self.game.snake.snake[0][1])
self.state[0] = int(diff[0] < 0)
self.state[2] = int(diff[0] > 1)
self.state[1] = int(diff[1] < 0)
self.state[3] = int(diff[1] > 0)
return self.state, self.reward, self.done, {}
def render(self):
self.game.render()
def reset(self):
self.game.reset()
class SnakeWrapper:
"""
return the croped square_size-by-square_size after rotation and changing to one-hot and doing block-notation.
"""
# num_classes is the number of different element types that can be found on the board.
# yes I know, actually we have 9 types, but 10 is nicer. (4 snakes + 1 obstacle + 3 fruits + 1 empty = 9)
num_classes = 10
# the action space. 0-left, 1-forward, 2-right.
action_space = gym.spaces.Discrete(3)
# the observation space. 9x9 one hot vectors, total 9x9x10.
# your snake always look up (the observation is a rotated crop of the board).
observation_space = gym.spaces.Box(
low=0,
high=num_classes,
shape=(9, 9, 10),
dtype=np.int
)
def __init__(self):
self.game = Game()
self.square_size = 9 # the observation size
self.timestep = 0
def step(self, action):
# get action as integer, move the game one step forward
# return tuple: state, reward, done, info. done is always False - Snake game never ends.
action = int_to_action[action]
reward = self.game.step(action)
head_pos = self.game.players[1].chain[-1]
direction = self.game.players[1].direction
board = self.game.board
state = preprocess_snake_state(board, head_pos, direction, self.square_size, SnakeWrapper.num_classes)
self.timestep += 1
return state, reward
def seed(self, seed=None):
return self.game.seed(seed)
# reset the game and return the board observation
def reset(self):
self.game.reset()
self.timestep = 0
first_state, _ = self.step(0)
return first_state
# print the board to the console
def render(self, mode='human'):
self.game.render(self.timestep)
# average fitness of a population
lstr(sum(n.fitness for n in networks) / len(networks)),
])
if generation >= max_generation:
break
# select for the next generation
networks = select_networks(networks)
# crossover for the next generation
networks = crossover_networks(networks)
# save data for the performance graph
with open("snake_data.csv", 'w') as file:
writer = csv.writer(file, delimiter=';') # delimit with semicolons
writer.writerows(snake_data)
# restore the best network
best_net = Network(net_args)
best_net.fitness = best_net_data[0]
best_net.import_data(best_net_data[1])
# run it
print(f"\nRunning best network: {best_net.fitness} fitness")
# attach the controller
game.external = partial(controller, best_net)
game.fps = 6
while not game.window.has_exit:
# reset the game
game.reset()
# run the game
game.run()
print(f"Score: {game.score}")
sleep(1)
