def act()

in habitat_baselines/agents/slam_agents.py [0:0]

• 41 lines of code
• 11 McCabe index (conditional complexity)

    def act(self, habitat_observation, random_prob=0.1):
        # Update internal state
        t = time.time()
        cc = 0
        update_is_ok = self.update_internal_state(habitat_observation)
        while not update_is_ok:
            update_is_ok = self.update_internal_state(habitat_observation)
            cc += 1
            if cc > 2:
                break
        if self.timing:
            print(time.time() - t, " s, update internal state")
        self.position_history.append(
            self.pose6D.detach().cpu().numpy().reshape(1, 4, 4)
        )
        success = self.is_goal_reached()
        if success:
            action = HabitatSimActions.STOP
            self.action_history.append(action)
            return {"action": action}
        # Plan action
        t = time.time()
        self.planned2Dpath, self.planned_waypoints = self.plan_path()
        if self.timing:
            print(time.time() - t, " s, Planning")
        t = time.time()
        # Act
        if self.waypointPose6D is None:
            self.waypointPose6D = self.get_valid_waypoint_pose6d()
        if (
            self.is_waypoint_reached(self.waypointPose6D)
            or not self.tracking_is_OK
        ):
            self.waypointPose6D = self.get_valid_waypoint_pose6d()
            self.waypoint_id += 1
        action = self.decide_what_to_do()
        # May be random?
        random_action = random.randint(0, self.num_actions - 1)
        what_to_do = np.random.uniform(0, 1, 1)
        if what_to_do < random_prob:
            action = random_action
        if self.timing:
            print(time.time() - t, " s, get action")
        self.action_history.append(action)
        return {"action": action}