def __init__(self,
env: AbstractEnvironment = Environment(),
_command_processors: List[AbstractCommandProcessor] = None):
self.__env = env
self.__command_processors: Dict[str, AbstractCommandProcessor] = {
x.keyword: x
for x in _command_processors or (command_processors +
[HelpCommandProcessor()])
}
def __init__(
self,
bot_controller: AbstractBotController = BotController.get_instance(),
room_state: AbstractRoomState = RoomState.get_instance(),
data_service: AbstractDataService = DataService(),
env: AbstractEnvironment = Environment()):
self.__bot_controller = bot_controller
self.__room_state = room_state
self.__data_service = data_service
self.__env = env
def __init__(self,
env: AbstractEnvironment = Environment(),
web_socket_client: AbstractWebSocketClient = WebSocketClient.
get_instance()):
if BotController.__instance:
raise Exception('Use get_instance() instead!')
self.__env = env
self.__web_socket_client = web_socket_client
self.__current_track: Optional[dict] = None
self.__doped: bool = False
self.__noped: bool = False
BotController.__instance = self
def eval_genomes(genomes, config):
genome_seed = random.randrange(0, 99999999)
print("Selected seed", genome_seed)
for genome_id, genome in genomes:
genome.fitness = 0
fov = 100
env = Environment(render=False,
max_projectiles=100,
seed=genome_seed,
scale=5,
fov_size=fov)
net = neat.nn.FeedForwardNetwork.create(genome, config)
while not env.finished:
raster = np.reshape(env.raster_array, (fov * 2, fov * 2))
# 40,000 = 200x200 needs to be reduced by 5x5 to size 1600 (40x40)
raster = block_reduce(raster, block_size=(5, 5), func=np.mean)
output = net.activate(raster.flatten())
env.take_action(np.argmax(
output)) # Take the action and update the game state (tick)
genome.fitness = env.fitness
from src.env import Environment
import matplotlib.pyplot as plt
import numpy as np
from skimage.measure import block_reduce
fov = 100
env = Environment(scale=5, fov_size=fov)
lasthash = None
while not env.finished:
env.take_action(3)
print(env.finished, env.age, env.fitness, env.hash)
if env.hash != lasthash:
lasthash = env.hash
raster = np.reshape(env.raster_array, (fov * 2, fov * 2))
# 40,000 = 200x200 needs to be reduced by 5x5 to size 1600 (40x40)
raster = block_reduce(raster, block_size=(5, 5), func=np.mean)
plt.imshow(raster, cmap='gray', interpolation='nearest')
plt.show()
'roomId': env.get_jqbx_room_id(),
'user': get_bot_user(env)
}))
def __on_message(message: WebSocketMessage) -> None:
logger.info('Incoming Message', message.as_dict())
try:
handler = web_socket_message_handler_map.get(message.label, None)
if handler:
handler.handle(message)
except Exception as e:
logger.error(e)
def __on_error(error: BaseException) -> None:
logger.error(error)
def __on_close() -> None:
logger.info('Websocket connection CLOSED')
while True:
web_socket_client.register(__on_open, __on_message, __on_error,
__on_close)
was_keyboard_interrupt = not web_socket_client.run()
if was_keyboard_interrupt:
break
logger.error(Exception('Websocket client stopped. Restarting.'))
if __name__ == '__main__':
main(WebSocketClient.get_instance(), Environment(), Logger())
from src.env import Environment
#env = Environment(render=True, scale=5, fov_size=100, keyboard=True)
#env.run()
fov = 100
env = Environment(scale=5,
max_projectiles=100,
fov_size=fov,
render=True,
keyboard=True)
env.run()
print(env.fitness)
# Do a 200x200 (fov_size = 100) field of view, but convert it into a bitmap which is 200x200/(10*10) = 400 (20x20)
# can be generated in O(N) time
def __init__(self, env: AbstractEnvironment = Environment()):
self.__base_url = env.get_data_service_base_url()
self.__room_id = env.get_jqbx_room_id()