def main():
"""Visualizes three cars in a merging scenario."""
from experiments.merging import ThreeLaneTestCar
from driver.car import FixedVelocityCar
from driver.world import ThreeLaneCarWorld
world = ThreeLaneCarWorld()
our_car = ThreeLaneTestCar(
world,
np.array([0, -0.5, 0.8, np.pi / 2]),
horizon=5,
weights=np.array([0.1, 0.0, 0.0, -10.0, -1.0, -10, -10]),
debug=True,
)
other_car_1 = FixedVelocityCar(
world,
np.array([0.1, -0.7, 0.8, np.pi / 2]),
horizon=5,
color="gray",
opacity=1.0,
debug=True,
)
other_car_2 = FixedVelocityCar(
world,
np.array([0.1, -0.2, 0.8, np.pi / 2]),
horizon=5,
color="gray",
opacity=1.0,
debug=True,
)
world.add_cars([our_car, other_car_1, other_car_2])
world.reset()
# vis.render(heatmap_show=False)
for i in range(5):
world.step()
print(i)
# vis.render(heatmap_show=False)
print([our_car.past_traj, other_car_1.past_traj, other_car_2.past_traj])
frames = [world.render(mode="rgb_array")]
import contextlib
with contextlib.redirect_stdout(None): # disable the pygame import print
from moviepy.editor import ImageSequenceClip
clip = ImageSequenceClip(frames, fps=int(1 / world.dt))
clip.speedx(0.5).write_gif("test_traj.gif", program="ffmpeg")
def execute_experiment(exp_name,
world,
time_steps,
experiment_args,
seed=None,
ms_car_index=0):
'''
Runs experiment.
Tracks reward per time step, which models are being used,
and the computational time for planning.
Displays and saves relevant time series information at end.
Saves GIF of experiment if requested.
Args:
world: Car World Object
time_steps: number of time steps to execute experiment for.
ms_car_index: index of model switching car (default 0)
save_gif: True iff you would like to save the simulation as a GIF.
'''
#Initialize planners if not yet done
for car in world.cars:
if (isinstance(car, PlannerCar) and car.planner is None):
car.initialize_planner()
if (world.verbose):
print(f"Executing {exp_name} for {time_steps} time steps...")
#Model Switching Car
ms_car = world.cars[ms_car_index]
world.reset(seed)
if (world.verbose):
world.render()
if (experiment_args.save_gif):
frames = []
frames.append(world.render("rgb_array"))
#Reward accrued at each time step.
reward_ts = []
for t in range(time_steps):
'''
Step world and get controls all cars
took and the new state of the world.
'''
_, control, new_state = world.step()
if (world.verbose):
world.render()
if (experiment_args.save_gif):
frames.append(world.render("rgb_array"))
ms_control = control[ms_car_index]
#Reward for the model switching car.
rew = ms_car.reward_fn(tf.stack(new_state), ms_control).numpy()
reward_ts.append(rew)
#Computational Time Breakdown
if (world.verbose):
ct_breakdown = ms_car.planner.avg_comp_time_breakdown()
print(f"T: {t + 1}; R: {rew:.2f}; CT: {ct_breakdown}")
if (experiment_args.num_run == 1):
model_ts = ms_car.planner.models_used
#Average Computational Time Time Series
avg_step_times = ms_car.planner.get_avg_comp_times()
print()
#Gather reward and computation time information
#Single Computational Time Time Series
step_times = ms_car.planner.get_comp_times()
#Display reward and computation time graphs
display_rewards(reward_ts, model_ts)
display_computational_times(step_times['overall'],
avg_step_times['overall'])
if (experiment_args.save_gif):
clip = ImageSequenceClip(frames, fps=int(1 / world.dt))
clip.speedx(0.5).write_gif(f"{exp_name}.gif", program="ffmpeg")
#return np.mean(reward_ts), avg_step_times['overall'][-1], model_usage
return reward_ts
def createGif(filename):
"""Create a gif using initial and edited images"""
clip = ImageSequenceClip([filename, 'images/editedImage.png'], fps=1350)
clip.speedx(0.5).write_gif('images/editedImage.gif')
