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)
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.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 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: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
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: 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:
# if self.num_steps_taken() == 0:
# self.env.render()
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: Union[int, Sequence[int]]) -> RLStepResult:
assert isinstance(action, int)
action = cast(int, action)
success = 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: 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
