def __init__(self,
drone_model: DroneModel = DroneModel.CF2X,
num_drones: int = 2,
neighbourhood_radius: float = np.inf,
initial_xyzs=None,
initial_rpys=None,
physics: Physics = Physics.PYB,
freq: int = 240,
aggregate_phy_steps: int = 1,
gui=False,
record=False,
obs: ObservationType = ObservationType.KIN,
act: ActionType = ActionType.RPM):
if num_drones < 2:
print(
"[ERROR] in BaseMultiagentAviary.__init__(), num_drones should be >= 2"
)
exit()
vision_attributes = True if obs == ObservationType.RGB else False
dynamics_attributes = True if act in [
ActionType.DYN, ActionType.ONE_D_DYN
] else False
self.OBS_TYPE = obs
self.ACT_TYPE = act
self.EPISODE_LEN_SEC = 5
#### Create integrated controllers #########################
if act in [ActionType.PID, ActionType.ONE_D_PID]:
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
if self.DRONE_MODEL in [DroneModel.CF2X, DroneModel.CF2P]:
self.ctrl = [
DSLPIDControl(BaseAviary(drone_model=DroneModel.CF2X))
for i in range(self.NUM_DRONES)
]
elif self.DRONE_MODEL == DroneModel.HB:
self.ctrl = [
SimplePIDControl(BaseAviary(drone_model=DroneModel.HB))
for i in range(self.NUM_DRONES)
]
super().__init__(
drone_model=drone_model,
num_drones=num_drones,
neighbourhood_radius=neighbourhood_radius,
initial_xyzs=initial_xyzs,
initial_rpys=initial_rpys,
physics=physics,
freq=freq,
aggregate_phy_steps=aggregate_phy_steps,
gui=gui,
record=record,
obstacles=
True, # Add obstacles for RGB observations and/or FlyThruGate
user_debug_gui=
False, # Remove of RPM sliders from all single agent learning aviaries
vision_attributes=vision_attributes,
dynamics_attributes=dynamics_attributes)
def __init__(self,
drone_model: DroneModel = DroneModel.CF2X,
num_drones: int = 2,
neighbourhood_radius: float = np.inf,
initial_xyzs=None,
initial_rpys=None,
physics: Physics = Physics.PYB,
freq: int = 240,
aggregate_phy_steps: int = 1,
gui=False,
record=False,
obs: ObservationType = ObservationType.KIN,
act: ActionType = ActionType.RPM):
"""Initialization of a generic multi-agent RL environment.
Attributes `vision_attributes` and `dynamics_attributes` are selected
based on the choice of `obs` and `act`; `obstacles` is set to True
and overridden with landmarks for vision applications;
`user_debug_gui` is set to False for performance.
Parameters
----------
drone_model : DroneModel, optional
The desired drone type (detailed in an .urdf file in folder `assets`).
num_drones : int, optional
The desired number of drones in the aviary.
neighbourhood_radius : float, optional
Radius used to compute the drones' adjacency matrix, in meters.
initial_xyzs: ndarray | None, optional
(NUM_DRONES, 3)-shaped array containing the initial XYZ position of the drones.
initial_rpys: ndarray | None, optional
(NUM_DRONES, 3)-shaped array containing the initial orientations of the drones (in radians).
physics : Physics, optional
The desired implementation of PyBullet physics/custom dynamics.
freq : int, optional
The frequency (Hz) at which the physics engine steps.
aggregate_phy_steps : int, optional
The number of physics steps within one call to `BaseAviary.step()`.
gui : bool, optional
Whether to use PyBullet's GUI.
record : bool, optional
Whether to save a video of the simulation in folder `files/videos/`.
obs : ObservationType, optional
The type of observation space (kinematic information or vision)
act : ActionType, optional
The type of action space (1 or 3D; RPMS, thurst and torques, or waypoint with PID control)
"""
if num_drones < 2:
print(
"[ERROR] in BaseMultiagentAviary.__init__(), num_drones should be >= 2"
)
exit()
vision_attributes = True if obs == ObservationType.RGB else False
dynamics_attributes = True if act in [
ActionType.DYN, ActionType.ONE_D_DYN
] else False
self.OBS_TYPE = obs
self.ACT_TYPE = act
self.EPISODE_LEN_SEC = 5
#### Create integrated controllers #########################
if act in [ActionType.PID, ActionType.ONE_D_PID]:
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
if self.DRONE_MODEL in [DroneModel.CF2X, DroneModel.CF2P]:
self.ctrl = [
DSLPIDControl(BaseAviary(drone_model=DroneModel.CF2X))
for i in range(self.NUM_DRONES)
]
elif self.DRONE_MODEL == DroneModel.HB:
self.ctrl = [
SimplePIDControl(BaseAviary(drone_model=DroneModel.HB))
for i in range(self.NUM_DRONES)
]
super().__init__(
drone_model=drone_model,
num_drones=num_drones,
neighbourhood_radius=neighbourhood_radius,
initial_xyzs=initial_xyzs,
initial_rpys=initial_rpys,
physics=physics,
freq=freq,
aggregate_phy_steps=aggregate_phy_steps,
gui=gui,
record=record,
obstacles=
True, # Add obstacles for RGB observations and/or FlyThruGate
user_debug_gui=
False, # Remove of RPM sliders from all single agent learning aviaries
vision_attributes=vision_attributes,
dynamics_attributes=dynamics_attributes)