def __init__(self, creator: TensorCreator, params: GridWorldParams):
super().__init__(creator.device)
self.validate_params(params)
self.params = params
# render a world map
tile_size = self.params.tile_size
width = self.params.world_width * self.params.tile_size
height = self.params.world_height * self.params.tile_size
self.bit_map = creator.zeros([height, width], dtype=torch.uint8)
if isinstance(creator, MeasuringCreator):
self.last_image = creator.zeros([height, width], dtype=self._float_dtype, device=self._device)
self.circle = creator.zeros([tile_size, tile_size], dtype=self._float_dtype, device=self._device)
self.circle_wall = creator.zeros([tile_size, tile_size, 3], dtype=self._float_dtype, device=self._device)
self.triangle_wall = creator.zeros([tile_size, tile_size, 3], dtype=self._float_dtype, device=self._device)
else:
self.circle = interpolate(creator.tensor(self.circle_template).view(1, 1, 9, 9),
size=[tile_size, tile_size]).view(tile_size, tile_size).type(torch.uint8)
self.triangle_wall = interpolate(creator.tensor(self.triangle_wall_template).view(1, 1, 9, 9),
size=[tile_size, tile_size]).view(tile_size, tile_size).type(torch.uint8)
self.circle_wall = interpolate(creator.tensor(self.circle_wall_template).view(1, 1, 9, 9),
size=[tile_size, tile_size]).view(tile_size, tile_size).type(torch.uint8)
self.bit_map_last_action = creator.zeros([height + 1, width], dtype=self._float_dtype, device=self._device)
self.bit_map_padding = width - 4
self.render_map(self.bit_map)
self.pos = creator.tensor(self.params.agent_pos[::-1], dtype=self._float_dtype, device=self._device)
self.last_position = creator.zeros(2, dtype=self._float_dtype, device=self._device)
self.last_position_one_hot_matrix = creator.zeros((params.world_height, params.world_width),
dtype=self._float_dtype, device=self._device)
self.last_action = creator.zeros(4, dtype=self._float_dtype, device=self._device)
self.reward = creator.zeros(1, dtype=self._float_dtype, device=self._device)
width = self.params.egocentric_width * self.params.tile_size
height = self.params.egocentric_height * self.params.tile_size
self.egocentric_image = creator.zeros((width, height), dtype=self._float_dtype, device=self._device)
self.ego_last_action = creator.zeros((width+1, height), dtype=self._float_dtype, device=self._device)
self.ego_padding = height - 4
if not isinstance(creator, MeasuringCreator):
# Initial rendering of the outputs, all further renderings will be just updates
self.last_image = creator.tensor(self.bit_map, dtype=self._float_dtype, device=self._device)
self.last_position.copy_(self.pos)
self._render_outputs()
def __init__(self,
creator: TensorCreator,
output_height: int,
output_width: int,
params: WeightedAvgNodeParams,
num_weights: int = 0):
super().__init__(creator.device)
self._params = params
self._creator = creator
if self._params.weights_on_input:
self.num_weights = num_weights
weights = np.ones((output_height, output_width, self.num_weights))
else:
assert len(
self._params.weights) != 0, 'Parametric weights not set!'
assert self._params.num_inputs == len(self._params.weights)
self.num_weights = len(self._params.weights)
weights = np.ones((output_height, output_width, self.num_weights))
for i in range(self.num_weights):
weights[i] = np.dot(weights[i], params.weights[i])
self.node_weights = creator.tensor(weights,
dtype=creator.float,
device=creator.device)
self.last_output = creator.zeros([output_width, output_height],
dtype=creator.float,
device=creator.device)
def __init__(self, creator: TensorCreator, noise_amplitude=0.2):
super().__init__(creator.device)
self._noise_amplitude = noise_amplitude
self.bitmap = creator.zeros([1, 2],
dtype=self._float_dtype,
device=self._device)
self._current_step = 0
self._data = creator.tensor([[0., 0], [1., 0], [0., 1], [1, 1]],
dtype=self._float_dtype,
device=self._device)
self._no_data = self._data.shape[1]
def _tensor_from_data(creator: TensorCreator):
return creator.tensor([1, 2, 3], dtype=creator.float64, device='cpu')