def __init__(self, observation_space, hidden_size):
super().__init__()
if (IntegratedPointGoalGPSAndCompassSensor.cls_uuid
in observation_space.spaces):
self._n_input_goal = observation_space.spaces[
IntegratedPointGoalGPSAndCompassSensor.cls_uuid].shape[0]
elif PointGoalSensor.cls_uuid in observation_space.spaces:
self._n_input_goal = observation_space.spaces[
PointGoalSensor.cls_uuid].shape[0]
elif ImageGoalSensor.cls_uuid in observation_space.spaces:
goal_observation_space = spaces.Dict(
{"rgb": observation_space.spaces[ImageGoalSensor.cls_uuid]})
self.goal_visual_encoder = SimpleCNN(goal_observation_space,
hidden_size)
self._n_input_goal = hidden_size
self._hidden_size = hidden_size
self.visual_encoder = SimpleCNN(observation_space, hidden_size)
self.state_encoder = RNNStateEncoder(
(0 if self.is_blind else self._hidden_size) + self._n_input_goal,
self._hidden_size,
)
self.train()
def __init__(
self,
observation_space: spaces.Dict,
hidden_size: int,
goal_hidden_size,
fuse_states,
force_blind,
normalize_vis,
normalize_state,
):
super().__init__()
if TargetPointGoalGPSAndCompassSensor.cls_uuid in observation_space.spaces:
self._n_input_goal = observation_space.spaces[
TargetPointGoalGPSAndCompassSensor.cls_uuid].shape[0]
elif (IntegratedPointGoalGPSAndCompassSensor.cls_uuid
in observation_space.spaces):
self._n_input_goal = observation_space.spaces[
IntegratedPointGoalGPSAndCompassSensor.cls_uuid].shape[0]
elif PointGoalSensor.cls_uuid in observation_space.spaces:
self._n_input_goal = observation_space.spaces[
PointGoalSensor.cls_uuid].shape[0]
elif ImageGoalSensor.cls_uuid in observation_space.spaces:
goal_observation_space = spaces.Dict(
{"rgb": observation_space.spaces[ImageGoalSensor.cls_uuid]})
self.goal_visual_encoder = SimpleCNN(goal_observation_space,
hidden_size, False, False)
self._n_input_goal = hidden_size
else:
self.fuse_states = fuse_states
self._n_input_goal = sum([
observation_space.spaces[n].shape[0] for n in self.fuse_states
])
self._hidden_size = hidden_size
self._goal_hidden_size = goal_hidden_size
self.visual_encoder = SimpleCNN(observation_space, hidden_size,
force_blind, normalize_vis)
state_dim = self._n_input_goal
if self._goal_hidden_size != 0:
self.goal_encoder = nn.Sequential(
nn.Linear(self._n_input_goal, self._goal_hidden_size),
nn.ReLU(),
nn.Linear(self._goal_hidden_size, self._goal_hidden_size),
nn.ReLU())
state_dim = self._goal_hidden_size
if normalize_state:
self.state_norm = RunningMeanAndVar(self._n_input_goal, [])
else:
self.state_norm = nn.Sequential()
self.state_encoder = build_rnn_state_encoder(
(0 if self.is_blind else self._hidden_size) + state_dim,
self._hidden_size,
)
self.train()
def __init__(self, observation_space, hidden_size, goal_sensor_uuid):
super().__init__()
self.goal_sensor_uuid = goal_sensor_uuid
self._n_input_goal = observation_space.spaces[self.goal_sensor_uuid].shape[0]
self._hidden_size = hidden_size
self.visual_encoder = SimpleCNN(observation_space, hidden_size)
self.state_encoder = RNNStateEncoder(
(0 if self.is_blind else self._hidden_size) + self._n_input_goal,
self._hidden_size,
)
self.train()