def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
action_str = self.action_names()[action]
if action_str == END:
self._took_end_action = True
self._success = self._is_goal_in_range()
self.last_action_success = self._success
else:
self.env.step({"action": action_str})
self.last_action_success = self.env.last_action_success
pose = self.env.agent_state()
self.path.append({k: pose[k] for k in ["x", "y", "z"]})
self.task_info["followed_path"].append(pose)
if len(self.path) > 1:
self.travelled_distance += IThorEnvironment.position_dist(
p0=self.path[-1], p1=self.path[-2], ignore_y=True)
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={
"last_action_success": self.last_action_success,
"action": action
},
)
return step_result
def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
action_str = self.class_action_names()[action]
if action_str == END:
self._took_end_action = True
self._success = self.is_goal_object_visible()
self.last_action_success = self._success
else:
self.env.step({"action": action_str})
self.last_action_success = self.env.last_action_success
if (
not self.last_action_success
) and self._CACHED_LOCATIONS_FROM_WHICH_OBJECT_IS_VISIBLE is not None:
self.env.update_graph_with_failed_action(failed_action=action_str)
self.task_info["followed_path"].append(self.env.get_agent_location())
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={"last_action_success": self.last_action_success},
)
return step_result
def _step(self, action: Tuple[int, int]) -> RLStepResult:
assert isinstance(action, tuple)
action_str1 = self.action_names()[action[0]]
action_str2 = self.action_names()[action[1]]
self.env.step({"action": action_str1, "agentId": 0})
self.last_action_success1 = self.env.last_action_success
self.env.step({"action": action_str2, "agentId": 1})
self.last_action_success2 = self.env.last_action_success
pose1 = self.env.agent_state(0)
self.task_info["followed_path1"].append(pose1)
pose2 = self.env.agent_state(1)
self.task_info["followed_path2"].append(pose2)
self.last_geodesic_distance = self.env.distance_cache.find_distance(
self.env.scene_name,
{k: pose1[k] for k in ["x", "y", "z"]},
{k: pose2[k] for k in ["x", "y", "z"]},
self.env.distance_from_point_to_point,
)
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={
"last_action_success": [
self.last_action_success1,
self.last_action_success2,
],
"action": action,
},
)
return step_result
def _step(self, action: int) -> RLStepResult:
action_str = self.class_action_names()[action]
self._last_action_str = action_str
action_dict = {"action": action_str}
object_id = self.task_info["objectId"]
if action_str == PICKUP:
action_dict = {**action_dict, "object_id": object_id}
self.env.step(action_dict)
self.last_action_success = self.env.last_action_success
last_action_name = self._last_action_str
last_action_success = float(self.last_action_success)
self.action_sequence_and_success.append((last_action_name, last_action_success))
self.visualize(last_action_name)
# If the object has not been picked up yet and it was picked up in the previous step update parameters to integrate it into reward
if not self.object_picked_up:
if (
object_id
in self.env.controller.last_event.metadata["arm"]["pickupableObjects"]
):
self.env.step(dict(action="PickupObject"))
# we are doing an additional pass here, label is not right and if we fail we will do it twice
object_inventory = self.env.controller.last_event.metadata["arm"][
"heldObjects"
]
if len(object_inventory) > 0 and object_id not in object_inventory:
self.env.step(dict(action="ReleaseObject"))
if self.env.is_object_at_low_level_hand(object_id):
self.object_picked_up = True
self.eplen_pickup = (
self._num_steps_taken + 1
) # plus one because this step has not been counted yet
if self.object_picked_up:
object_state = self.env.get_object_by_id(object_id)
goal_state = self.task_info["target_location"]
goal_achieved = self.object_picked_up and self.obj_state_aproximity(
object_state, goal_state
)
if goal_achieved:
self._took_end_action = True
self.last_action_success = goal_achieved
self._success = goal_achieved
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={"last_action_success": self.last_action_success},
)
return step_result
def _step(self, action: Sequence[float]) -> RLStepResult:
action = np.array(action)
gym_obs, reward, self._gym_done, info = self.env.step(action=action)
return RLStepResult(
observation=self.get_observations(gym_obs=gym_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def step(self, action: int) -> RLStepResult:
step_result = super().step(action=action)
if self.greedy_expert is not None:
self.greedy_expert.update(
action_taken=action,
action_success=step_result.info["action_success"])
step_result = RLStepResult(
observation=self.get_observations(),
reward=step_result.reward,
done=step_result.done,
info=step_result.info,
)
return step_result
def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
action_str = self.action_names()[action]
if action_str == END:
self._took_end_action = True
self._success = self._is_goal_in_range()
self.last_action_success = self._success
elif action_str in [
DIRECTIONAL_AHEAD_PUSH, DIRECTIONAL_BACK_PUSH,
DIRECTIONAL_RIGHT_PUSH, DIRECTIONAL_LEFT_PUSH
]:
angle = [0.001, 180, 90, 270][action - 5]
obj = self.env.moveable_closest_obj_by_types(
self.task_info["obstacles_types"])
if obj != None:
self.env.step({
"action": action_str,
"objectId": obj["objectId"],
"moveMagnitude": obj["mass"] * 100,
"pushAngle": angle,
"autoSimulation": False
})
self.last_action_success = self.env.last_action_success
else:
self.last_action_success = False
elif action_str in [LOOK_UP, LOOK_DOWN]:
self.env.step({"action": action_str})
self.last_action_success = self.env.last_action_success
else:
self.env.step({"action": action_str})
self.last_action_success = self.env.last_action_success
pose = self.env.agent_state()
self.path.append({k: pose[k] for k in ["x", "y", "z"]})
self.task_info["followed_path"].append(pose)
if len(self.path) > 1 and self.path[-1] != self.path[-2]:
self.num_moves_made += 1
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={
"last_action_success": self.last_action_success,
"action": action
},
)
return step_result
def _step(self, action: int) -> RLStepResult:
assert isinstance(action, int)
minigrid_obs, reward, done, info = self.env.step(action=action)
self._last_action = action
self._was_successful = done and reward > 0
return RLStepResult(
observation=self.get_observations(
minigrid_output_obs=minigrid_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def _step(self, action: int) -> RLStepResult:
"""Take a step in the task.
# Parameters
action: is the index of the action from self.action_names()
"""
# parse the action data
action_name = self.action_names()[action]
if action_name.startswith("pickup"):
# Don't allow the exploration agent to pickup objects
action_success = False
elif action_name.startswith("open_by_type"):
# Don't allow the exploration agent to open objects
action_success = False
elif action_name.startswith(
("move", "rotate", "look", "stand", "crouch")):
# take the movement action
action_success = getattr(self.walkthrough_env, action_name)()
elif action_name == "drop_held_object_with_snap":
# Don't allow the exploration agent to drop objects (not that it can hold any)
action_success = False
elif action_name == "done":
self._took_end_action = True
action_success = True
else:
raise RuntimeError(
f"Action '{action_name}' is not in the action space {RearrangeActionSpace}"
)
self.actions_taken.append(action_name)
self.actions_taken_success.append(action_success)
return RLStepResult(
observation=self.get_observations(),
reward=self._judge(action_name=action_name,
action_success=action_success),
done=self.is_done(),
info={
"action_name": action_name,
"action_success": action_success
},
)
def _step(self, action: int) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
minigrid_obs, reward, self._minigrid_done, info = self.env.step(
action=self._ACTION_IND_TO_MINIGRID_IND[action])
# self.env.render()
return RLStepResult(
observation=self.get_observations(
minigrid_output_obs=minigrid_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def _step(self, action: Dict[str, int]) -> RLStepResult:
assert len(action) == 2, "got action={}".format(action)
minigrid_obs, reward, self._minigrid_done, info = self.env.step(
action=(
self._ACTION_IND_TO_MINIGRID_IND[action["lower"] + 2 * action["higher"]]
)
)
# self.env.render()
return RLStepResult(
observation=self.get_observations(minigrid_output_obs=minigrid_obs),
reward=reward,
done=self.is_done(),
info=info,
)
def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
old_pos = self.get_observations()["agent_position_and_rotation"]
action_str = self.action_names()[action]
self._actions_taken.append(action_str)
self.env.step({"action": action_str})
# if action_str != END:
# self.env.step({"action": action_str})
# if self.env.env.get_metrics()['distance_to_goal'] <= 0.2:
# self._took_end_action = True
# self._success = self.env.env.get_metrics()['distance_to_goal'] <= 0.2
# self.last_action_success = self._success
# else:
# self.last_action_success = self.env.last_action_success
if action_str == END:
self._took_end_action = True
self._success = self._is_goal_in_range()
self.last_action_success = self._success
else:
self.last_action_success = self.env.last_action_success
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={"last_action_success": self.last_action_success},
)
new_pos = self.get_observations()["agent_position_and_rotation"]
if np.all(old_pos == new_pos):
self._num_invalid_actions += 1
pos = self.get_observations()["agent_position_and_rotation"]
self._positions.append({
"x": pos[0],
"y": pos[1],
"z": pos[2],
"rotation": pos[3]
})
return step_result
def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
self.env.step(action)
reward = STEP_PENALTY
if np.all(self.env.current_position == self.env.goal_position):
self._found_target = True
reward += FOUND_TARGET_REWARD
elif self.num_steps_taken() == self.max_steps - 1:
reward = STEP_PENALTY / (1 - DISCOUNT_FACTOR)
return RLStepResult(
observation=self.get_observations(),
reward=reward,
done=self.is_done(),
info=None,
)
def _step(self, action: int) -> RLStepResult:
action_str = self.class_action_names()[action]
self._last_action_str = action_str
action_dict = {"action": action_str}
object_id = self.task_info["objectId"]
if action_str == PICKUP:
action_dict = {**action_dict, "object_id": object_id}
self.env.step(action_dict)
self.last_action_success = self.env.last_action_success
last_action_name = self._last_action_str
last_action_success = float(self.last_action_success)
self.action_sequence_and_success.append(
(last_action_name, last_action_success))
self.visualize(last_action_name)
# If the object has not been picked up yet and it was picked up in the previous step update parameters to integrate it into reward
if not self.object_picked_up:
if self.env.is_object_at_low_level_hand(object_id):
self.object_picked_up = True
self.eplen_pickup = (
self._num_steps_taken + 1
) # plus one because this step has not been counted yet
if action_str == DONE:
self._took_end_action = True
object_state = self.env.get_object_by_id(object_id)
goal_state = self.task_info["target_location"]
goal_achieved = self.object_picked_up and self.obj_state_aproximity(
object_state, goal_state)
self.last_action_success = goal_achieved
self._success = goal_achieved
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={"last_action_success": self.last_action_success},
)
return step_result
def _step(self, action: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
action_str = self.class_action_names()[action]
self.env.step({"action": action_str})
if action_str == END:
self._took_end_action = True
self._success = self._is_goal_in_range()
self.last_action_success = self._success
else:
self.last_action_success = self.env.last_action_success
step_result = RLStepResult(
observation=self.get_observations(),
reward=self.judge(),
done=self.is_done(),
info={"last_action_success": self.last_action_success},
)
return step_result
def _step(self, action: int) -> RLStepResult:
"""
action : is the index of the action from self.action_names()
"""
# parse the action data
action_name = self.action_names()[action]
if action_name.startswith("pickup"):
# NOTE: due to the object_id's not being in the metadata for speedups,
# they cannot be targeted with interactible actions. Hence, why
# we're resetting the object filter before targeting by object id.
with include_object_data(self.unshuffle_env.controller):
metadata = self.unshuffle_env.last_event.metadata
if len(metadata["inventoryObjects"]) != 0:
action_success = False
else:
object_type = stringcase.pascalcase(
action_name.replace("pickup_", ""))
possible_objects = [
o for o in metadata["objects"]
if o["visible"] and o["objectType"] == object_type
]
possible_objects = sorted(possible_objects,
key=lambda po:
(po["distance"], po["name"]))
object_before = None
if len(possible_objects) > 0:
object_before = possible_objects[0]
object_id = object_before["objectId"]
if object_before is not None:
self.unshuffle_env.controller.step(
"PickupObject",
objectId=object_id,
**self.unshuffle_env.physics_step_kwargs,
)
action_success = self.unshuffle_env.last_event.metadata[
"lastActionSuccess"]
else:
action_success = False
if action_success and self.unshuffle_env.held_object is None:
get_logger().warning(
f"`PickupObject` was successful in picking up {object_id} but we're not holding"
f" any objects! Current task spec:\n{self.unshuffle_env.current_task_spec}."
)
action_success = False
elif action_name.startswith("open_by_type"):
object_type = stringcase.pascalcase(
action_name.replace("open_by_type_", ""))
with include_object_data(self.unshuffle_env.controller):
obj_name_to_goal_and_cur_poses = {
cur_pose["name"]: (goal_pose, cur_pose)
for _, goal_pose, cur_pose in zip(
*self.unshuffle_env.poses)
}
goal_pose = None
cur_pose = None
for o in self.unshuffle_env.last_event.metadata["objects"]:
if (o["visible"] and o["objectType"] == object_type
and o["openable"]
and not self.unshuffle_env.are_poses_equal(
*obj_name_to_goal_and_cur_poses[o["name"]])):
goal_pose, cur_pose = obj_name_to_goal_and_cur_poses[
o["name"]]
break
if goal_pose is not None:
object_id = cur_pose["objectId"]
goal_openness = goal_pose["openness"]
if cur_pose["openness"] > 0.0:
self.unshuffle_env.controller.step(
"CloseObject",
objectId=object_id,
**self.unshuffle_env.physics_step_kwargs,
)
self.unshuffle_env.controller.step(
"OpenObject",
objectId=object_id,
openness=goal_openness,
**self.unshuffle_env.physics_step_kwargs,
)
action_success = self.unshuffle_env.last_event.metadata[
"lastActionSuccess"]
else:
action_success = False
elif action_name.startswith(
("move", "rotate", "look", "stand", "crouch")):
# apply to only the unshuffle env as the walkthrough agent's position
# must now be managed by the whichever sensor is trying to read data from it.
action_success = getattr(self.unshuffle_env, action_name)()
elif action_name == "drop_held_object_with_snap":
action_success = getattr(self.unshuffle_env, action_name)()
elif action_name == "done":
self._took_end_action = True
action_success = True
elif action_name == "pass":
action_success = True
else:
raise RuntimeError(
f"Action '{action_name}' is not in the action space {RearrangeActionSpace}"
)
self.actions_taken.append(action_name)
self.actions_taken_success.append(action_success)
if self.task_spec_in_metrics:
self.agent_locs.append(self.unshuffle_env.get_agent_location())
return RLStepResult(
observation=None,
reward=self._judge(),
done=self.is_done(),
info={
"action_name": action_name,
"action_success": action_success
},
)
