Adding a wind field to quadcopter RL training python code

Omniverse Isaac Sim
1

Hello, I am now looking to add a wind field to the quadcopter reinforcement learning training process and implement it in python code. Here is the python code I see.

                # Create the force field prim
                xformPrim = UsdGeom.Xform.Define(stage, primPathName)
                xformPrim.AddTranslateOp().Set(Gf.Vec3f(0.0, 500.0, 0.0))
                prim = xformPrim.GetPrim()

                # Add the wind force fields to the prim
                windApi = ForceFieldSchema.PhysxForceFieldWindAPI.Apply(prim, "wind")
                windApi.CreateDragAttr(1000.0)
                windApi.CreateAverageSpeedAttr(500.0)
                windApi.CreateSpeedVariationAttr(2.0)
                windApi.CreateSpeedVariationFrequencyAttr(3.0)
                windApi.CreateAverageDirectionAttr(Gf.Vec3f(0.0, 0.0, 1.0))
                windApi.CreateDirectionVariationAttr(Gf.Vec3f(0.707, 0.707, 0.0))
                windApi.CreateDirectionVariationFrequencyAttr(Gf.Vec3f(2.0, 2.0, 2.0))
                forceFieldApi = ForceFieldSchema.PhysxForceFieldAPI(windApi, "wind")
                forceFieldApi.CreateEnabledAttr(True)
                forceFieldApi.CreatePositionAttr(Gf.Vec3f(0.0, 0.0, 0.0))
                forceFieldApi.CreateRangeAttr(Gf.Vec2f(-1.0, -1.0))

                forceFieldApi.CreateSurfaceSampleDensityAttr(surfaceSampleDensity)

But I don’t know where I should put them in quadcopter_env.py.

class QuadcopterEnvCfg(DirectRLEnvCfg):
    # env
    episode_length_s = 10.0
    decimation = 2
    action_space = 4
    observation_space = 12
    state_space = 0
    debug_vis = True

    ui_window_class_type = QuadcopterEnvWindow

    # simulation
    sim: SimulationCfg = SimulationCfg(
        dt=1 / 100,
        render_interval=decimation,
        disable_contact_processing=True,
        physics_material=sim_utils.RigidBodyMaterialCfg(
            friction_combine_mode="multiply",
            restitution_combine_mode="multiply",
            static_friction=1.0,
            dynamic_friction=1.0,
            restitution=0.0,
        ),
    )
    terrain = TerrainImporterCfg(
        prim_path="/World/ground",
        terrain_type="plane",
        collision_group=-1,
        physics_material=sim_utils.RigidBodyMaterialCfg(
            friction_combine_mode="multiply",
            restitution_combine_mode="multiply",
            static_friction=1.0,
            dynamic_friction=1.0,
            restitution=0.0,
        ),
        debug_vis=False,
    )

    # scene
    scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=4096, env_spacing=2.5, replicate_physics=True)

    # robot
    robot: ArticulationCfg = CRAZYFLIE_CFG.replace(prim_path="/World/envs/env_.*/Robot")
    thrust_to_weight = 1.9
    moment_scale = 0.01

    # reward scales
    lin_vel_reward_scale = -0.05
    ang_vel_reward_scale = -0.01
    distance_to_goal_reward_scale = 15.0


class QuadcopterEnv(DirectRLEnv):
    cfg: QuadcopterEnvCfg

    def __init__(self, cfg: QuadcopterEnvCfg, render_mode: str | None = None, **kwargs):
        super().__init__(cfg, render_mode, **kwargs)

        # Total thrust and moment applied to the base of the quadcopter
        self._actions = torch.zeros(self.num_envs, gym.spaces.flatdim(self.single_action_space), device=self.device)
        self._thrust = torch.zeros(self.num_envs, 1, 3, device=self.device)
        self._moment = torch.zeros(self.num_envs, 1, 3, device=self.device)
        # Goal position
        self._desired_pos_w = torch.zeros(self.num_envs, 3, device=self.device)

        # Logging
        self._episode_sums = {
            key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
            for key in [
                "lin_vel",
                "ang_vel",
                "distance_to_goal",
            ]
        }
        # Get specific body indices
        self._body_id = self._robot.find_bodies("body")[0]
        self._robot_mass = self._robot.root_physx_view.get_masses()[0].sum()
        self._gravity_magnitude = torch.tensor(self.sim.cfg.gravity, device=self.device).norm()
        self._robot_weight = (self._robot_mass * self._gravity_magnitude).item()

        # add handle for debug visualization (this is set to a valid handle inside set_debug_vis)
        self.set_debug_vis(self.cfg.debug_vis)

    def _setup_scene(self):
        self._robot = Articulation(self.cfg.robot)
        self.scene.articulations["robot"] = self._robot

        self.cfg.terrain.num_envs = self.scene.cfg.num_envs
        self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing
        self._terrain = self.cfg.terrain.class_type(self.cfg.terrain)
        # clone, filter, and replicate
        self.scene.clone_environments(copy_from_source=False)
        self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path])
        # add lights
        light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75))
        light_cfg.func("/World/Light", light_cfg)

Dear
yoko