def __init__(self, game):
self.environment = None
self.game = game
self.dim = cfg.environment()['dim']
self.resolution = cfg.qlearing()['resolution']
self.alpha = cfg.qlearing()['alpha']
self.epsilon = cfg.qlearing()['epsilon']
self.gamma = cfg.qlearing()['gamma']
self.LEARNING = cfg.qlearing()['learning']
self.NEW_QTABLE = cfg.qlearing()['new_qtable']
self.number_of_directions = cfg.qlearing()['number_of_directions']
self.qtable = self.load_qtable()
self.frames = 0
self.epoch = 0
self.max_epochs = cfg.qlearing()['max_epochs']
self.max_frames = cfg.qlearing()['max_ticks']
# Debug
self.grid = cfg.qlearing()['print_grid']
self.arrows = cfg.qlearing()['print_vectors']
self.QDEBUG_LAYER = 0
self.AGENT_INDEX_DEBUG = 0
if self.arrows:
self.ARROW_FISH_SPRITE, self.ARROW_PREDATOR_SPRITE = self.arrow_sprite(
)
self.stat_filename = None
if self.LEARNING:
self.setup_stat_file()
def kill_all_emigrants(self):
tolerance = cfg.environment()['border_tolerance']
emigrants = []
self.fish_qtree.set_mask(gen_border('top', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.fish_qtree.elements()]
]
self.fish_qtree.set_mask(gen_border('right', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.fish_qtree.elements()]
]
self.fish_qtree.set_mask(gen_border('bottom', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.fish_qtree.elements()]
]
self.fish_qtree.set_mask(gen_border('left', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.fish_qtree.elements()]
]
if cfg.qlearing()['predators_learning']:
self.predator_qtree.set_mask(gen_border('top',
tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.predator_qtree.elements()]
]
self.predator_qtree.set_mask(
gen_border('right', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.predator_qtree.elements()]
]
self.predator_qtree.set_mask(
gen_border('bottom', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.predator_qtree.elements()]
]
self.predator_qtree.set_mask(
gen_border('left', tolerance=tolerance))
[
emigrants.append(emigrant) for emigrant in
[element[2] for element in self.predator_qtree.elements()]
]
[emigrant.die() for emigrant in emigrants]
def gen_border(*args, **kwargs):
w, h = cfg.environment()['dim']
tolerance = kwargs['tolerance']
if 'top' in args:
return np.array([[-tolerance, -tolerance], [w + tolerance, -tolerance],
[w + tolerance, tolerance], [-tolerance, tolerance]])
elif 'right' in args:
return np.array([[w - tolerance, -tolerance],
[w + tolerance, -tolerance],
[w + tolerance, h + tolerance],
[w - tolerance, h + tolerance]])
elif 'bottom' in args:
return np.array([[-tolerance, -tolerance + h],
[w + tolerance, -tolerance + h],
[w + tolerance, tolerance + h],
[-tolerance, tolerance + h]])
elif 'left':
return np.array([[-tolerance, -tolerance], [tolerance, -tolerance],
[tolerance, h + tolerance],
[-tolerance, h + tolerance]])
else:
raise NotImplemented()
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def resize(image: np.ndarray):
'''Resizes boid view to fit neural network input size'''
r = cfg.dqn_vision()['view_size']
b_size = int(round(image.shape[0] / r))
return block_reduce(image, block_size=(b_size, b_size), func=np.mean)
def rgb_to_normalized_gray(rgb):
'''Normalizes and maps 3 rgb channels to 1 channel grayscale'''
return np.dot(rgb[..., :3] / 255, [0.2989, 0.5870, 0.1140])
screen_width, screen_height = cfg.environment()['dim']
def extract_boid_view(vision_area: np.ndarray,
position: np.ndarray) -> torch.Tensor:
'''Computes downsampled boid view image state'''
vision_poly = boid_vision_poly(vision_area, position)
return downsample_image(vision_poly)
def boid_vision_poly(vision_area: np.ndarray,
position: np.ndarray) -> torch.Tensor:
'''Cutting boid reaction area from game screen'''
screen = pg.display.get_surface()
for point in vision_area:
def random_position():
borders = cfg.borders()
tolerance = cfg.environment()['border_tolerance']
return pg.Vector2(
scale(np.random.rand(), [0, 1], [tolerance, borders[0] - tolerance]),
scale(np.random.rand(), [0, 1], [tolerance, borders[1] - tolerance]))
def random_velocity(magnitude):
angle = scale(np.random.rand(), [0, 1], [0, 360])
vec = pg.Vector2()
vec.from_polar((magnitude, angle))
return vec
def scale(value, old, new):
return ((value - old[0]) / (old[1] - old[0])) * (new[1] - new[0]) + new[0]
X_AXIS_VEC = pg.Vector2(1, 0)
Y_AXIS_VEC = pg.Vector2(0, 1)
DEBUG_POSITION_COLOR = pg.Color('green')
SCREEN_WIDTH, SCREEN_HEIGHT = cfg.environment()['dim']
class Agent:
def __init__(self, sprite: pg.Surface, position: pg.Vector2,
velocity: pg.Vector2, reaction_radius: float):
self.original_sprite = sprite
self.showable_sprite = None
self.hitbox = None
self.position = position
self.velocity = velocity
self.acceleration = pg.Vector2(0, 0)
self.alive = True
self.closest_target = None
self.last_observation = None
self.current_observation = None
return np.array([[-tolerance, -tolerance + h],
[w + tolerance, -tolerance + h],
[w + tolerance, tolerance + h],
[-tolerance, tolerance + h]])
elif 'left':
return np.array([[-tolerance, -tolerance], [tolerance, -tolerance],
[tolerance, h + tolerance],
[-tolerance, h + tolerance]])
else:
raise NotImplemented()
BORDERS_NAMES = ['top', 'right', 'bottom', 'left']
TURNING_BORDERS: List[Polygon] = [
Polygon(
gen_border(border, tolerance=cfg.environment()['turning_tolerance']))
for border in BORDERS_NAMES
]
SCREEN_WIDTH, SCREEN_HEIGHT = cfg.environment()['dim']
class Environment:
def __init__(self):
self.fishes = [Fish() for _ in range(cfg.fish_amount())]
self.all_fishes = self.fishes
self.predators = [Predator() for _ in range(cfg.predator_amount())]
self.all_predators = self.predators
self.fish_qtree = None
self.predator_qtree = None
self.last_states = {
'all_fishes': self.fishes,