OS: Ubuntu 18.04.4 LTS
Using AirSim Neighborhood binary v1.3.0-Linux
Python version: 3.6.9
My hardware:
Dear developers,
I'm trying to achieve an AirSim RL environment (much like the OpenAI environments). Following the car RL example in the documentation, I've created the following class below. However I stumbled upon some issues:
-
I'm trying to achieve a high frame rate from the client.simGetImages() for depthview. The highest frame rate I got was around 15 FPS with the following settings (below). I've tried to change the viewmode to "NoDisplay" but this drops the frame rate to 1 FPS and lowering the resolution in the CaptureSettings didn't help either.
-
Running the binary for a long time results sometimes in a sudden crash (log below), I'm running the binary as follows:
./AirSimNH.sh -vulkan -novsync -benchmark (also tried with -opengl4 flag, but eventually crashes as well)
-
Is there a way to disable newly added animals?
Much thanks in advance!
The Environment class with main loop to measure FPS:
class AirSim:
def __init__(self):
self.client = airsim.CarClient()
self.client.confirmConnection()
self.client.enableApiControl(True)
self.car_controls = airsim.CarControls()
def reconnect(self):
self.client = airsim.CarClient()
self.client.confirmConnection()
self.client.enableApiControl(True)
self.car_controls = airsim.CarControls()
# return depth view images (84x84)
def getImg(self):
responses = self.client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.DepthPerspective, True, False)])
img = self.transformInput(responses)
return img
def transformInput(self, responses):
img1d = np.array(responses[0].image_data_float, dtype=np.float)
img1d = 255 / np.maximum(np.ones(img1d.size), img1d)
img2d = np.reshape(img1d, (responses[0].height, responses[0].width))
from PIL import Image
image = Image.fromarray(img2d)
im_final = np.array(image.resize((84, 84)).convert('L'))
return im_final
def interpret_action(self, action):
self.car_controls.brake = 0
self.car_controls.throttle = 1
if action == 0:
self.car_controls.throttle = 0
self.car_controls.brake = 1
elif action == 1:
self.car_controls.steering = 0
elif action == 2:
self.car_controls.steering = 0.5
elif action == 3:
self.car_controls.steering = -0.5
elif action == 4:
self.car_controls.steering = 0.25
else:
self.car_controls.steering = -0.25
return self.car_controls
def compute_reward(self, car_state):
MAX_SPEED = 300
MIN_SPEED = 10
thresh_dist = 3.5
beta = 3
z = 0
pts = [np.array([0, -1, z]), np.array([130, -1, z]), np.array([130, 125, z]), np.array([0, 125, z]),
np.array([0, -1, z]), np.array([130, -1, z]), np.array([130, -128, z]), np.array([0, -128, z]),
np.array([0, -1, z])] # points in Neighborhood
pd = car_state.kinematics_estimated.position
car_pt = np.array([pd.x_val, pd.y_val, pd.z_val])
dist = 10000000
for i in range(0, len(pts) - 1):
dist = min(dist, np.linalg.norm(np.cross((car_pt - pts[i]), (car_pt - pts[i + 1]))) / np.linalg.norm(
pts[i] - pts[i + 1]))
# print(dist)
if dist > thresh_dist: # the max deviation
reward = float(-3)
else:
reward_dist = (
math.exp(-beta * dist) - 0.5) # dist=3.5 -> reward_dist≃-0.5 dist=0 -> reward_dist≃0.5
reward_speed = (((car_state.speed - MIN_SPEED) / (
MAX_SPEED - MIN_SPEED)) - 0.5) # max reward_speed = 1 min reward_speed ≃ -0.5
reward = reward_dist + reward_speed # min reward >≃ -1 max reward = 1
return reward
def isDone(self, car_state, car_controls, reward):
done = 0
if reward < -1.5:
done = 1
if car_controls.brake == 0:
if car_state.speed <= 5:
done = 1
return done
def step(self, action):
#interpret action (number) to actual car_controls
car_controls = self.interpret_action(action)
self.client.setCarControls(car_controls)
car_state = self.client.getCarState()
reward = self.compute_reward(car_state)
done = self.isDone(car_state, car_controls, reward)
img = self.getImg()
#punish harder if episode is done
if done == 1:
reward = float(-10)
return img, reward, done, car_state
def reset(self):
self.client.reset()
self.client.enableApiControl(True)
self.client.armDisarm(True)
car_control = self.interpret_action(1) # Reset position and drive straight for one second
self.client.setCarControls(car_control)
time.sleep(5)
return self.getImg()
if __name__ == '__main__':
env = AirSim()
state = env.reset()
while True:
start_time = time.time() # start time of the loop
img, reward, done, car_state = env.step(1) # each step() calls for client.simGetImages()
if done:
env.reset()
print("FPS: ", 1.0 / (time.time() - start_time)) # FPS = 1 / time to process loopSettings.json:
settings.txt
Crash log:
log.txt
OS: Ubuntu 18.04.4 LTS
Using AirSim Neighborhood binary v1.3.0-Linux
Python version: 3.6.9
My hardware:
Dear developers,
I'm trying to achieve an AirSim RL environment (much like the OpenAI environments). Following the car RL example in the documentation, I've created the following class below. However I stumbled upon some issues:
I'm trying to achieve a high frame rate from the client.simGetImages() for depthview. The highest frame rate I got was around 15 FPS with the following settings (below). I've tried to change the viewmode to "NoDisplay" but this drops the frame rate to 1 FPS and lowering the resolution in the CaptureSettings didn't help either.
Running the binary for a long time results sometimes in a sudden crash (log below), I'm running the binary as follows:
./AirSimNH.sh -vulkan -novsync -benchmark (also tried with -opengl4 flag, but eventually crashes as well)
Is there a way to disable newly added animals?
Much thanks in advance!
The Environment class with main loop to measure FPS:
Settings.json:
settings.txt
Crash log:
log.txt