def main():
with tf.Session() as sess:
while True:
try:
env = CarlaEnv()
break
except Exception as e:
print(e)
agent = Agent(sess=sess,
state_size=env.observation_space.shape[0],
action_size=env.action_space.shape[0])
max_episodes = 1000
max_steps = 1800
for i in range(int(max_episodes)):
state = env.reset()
print(state.shape)
ep_reward = 0
ep_ave_max_q = 0
# plt.clf()
# if i:
# with open("ddpg_memory.pkl","wb") as hand:
# pickle.dump(replay_buffer,hand)
# actor.save_model()
# critic.save_model()
# print("Agent saved")
for j in range(int(max_steps)):
print("epoch: {}, step: {}".format(i, j))
# env.render()
# Added exploration noise
# a = actor.predict(np.reshape(s, (1, 3))) + (1. / (1. + i))
action = agent.get_action(state)
# a = controller(s[0],s[1],s[3])
# a = [a]
next_state, reward, done, info = env.step(action)
print("reward: {}".format(reward))
agent.remember(state, action, reward, done, next_state)
# Keep adding experience to the memory until
# there are at least minibatch size samples
agent.train()
def main():
np.random.seed(1337)
env = CarlaEnv()
for _ in range(EPISODES):
SAVE_PATH.mkdir(exist_ok=True)
collect_episode(env,
SAVE_PATH / ('%03d' % len(list(SAVE_PATH.glob('*')))))
def main():
np.random.seed(1337)
for i in range(1, 8):
with CarlaEnv(town='Town0%s' % i) as env:
for episode in range(EPISODES):
env.reset(n_vehicles=np.random.choice([50, 100, 200]),
n_pedestrians=np.random.choice([50, 100, 200]),
seed=np.random.randint(0, 256))
env._player.set_autopilot(True)
collect_episode(
env, SAVE_PATH / ('%03d' % len(list(SAVE_PATH.glob('*')))))
def main(num_runs):
env = None
RENDER = True
MAX_STEPS_PER_EPISODE = 300
WARMING_UP_STEPS = 50
WINDOW_SIZE = 5
RETURN_SEQUENCE = False
GATHER_DATA = True
SAVE_INFO = True
TRAINING = True
TEST = False
try:
pygame.init()
pygame.font.init()
# create environment
env = CarlaEnv(render_pygame=RENDER,warming_up_steps=WARMING_UP_STEPS,window_size=WINDOW_SIZE)
max_steps_per_episode = MAX_STEPS_PER_EPISODE
if GATHER_DATA:
gather_data(env, num_runs=10, max_steps_per_episode=max_steps_per_episode, save_info=SAVE_INFO)
elif TRAINING:
pass
elif TEST:
pass
finally:
if env and env.world is not None:
env.world.destroy()
# env.world.destroy_all_actors()
# env.sych_distroy()
settings = env._carla_world.get_settings()
settings.synchronous_mode = False
env._carla_world.apply_settings(settings)
print('\ndisabling synchronous mode.')
pygame.quit()
def main(num_runs):
env = None
RENDER = True
MAX_STEPS_PER_EPISODE = 300
SAVE_INFO = True
try:
quit_flag = False
pygame.init()
pygame.font.init()
# create in
env = CarlaEnv(render_pygame=RENDER)
max_steps_per_episode = MAX_STEPS_PER_EPISODE
clock = pygame.time.Clock()
CAV_infos = []
HDV_infos = []
for episode in range(num_runs):
state = env.reset()
episode_reward = 0
for timestep in range(max_steps_per_episode):
clock.tick()
env.world.tick(clock)
# check quit
for event in pygame.event.get():
if event.type == pygame.QUIT:
quit_flag = True
rl_actions = np.random.choice(3,2)
state, reward, done, _ = env.step(rl_actions)
# print(state)
cav_control = env.world.cav_controller.current_control
# print("current control: ", cav_control)
# print("carla control: ", env.world.CAV.get_control())
# print()
for veh_id, state_vals in state.items():
if veh_id == 'CAV':
CAV_info = [veh_id,episode,timestep] +state_vals+ list(cav_control.values())
CAV_infos.append(CAV_info)
else:
hdv_info = [veh_id,episode,timestep] + state_vals
HDV_infos.append(hdv_info)
episode_reward += reward
if done:
break
if quit_flag:
print("stop in the middle ... ")
return
print("done in : ", timestep, " -- episode reward: ", episode_reward)
# time.sleep(0.01)
finally:
if SAVE_INFO:
cav_info = pd.DataFrame(CAV_infos,columns=['veh_id','episode','episode_step','px','py','sx','sy','ax','ay','throttle','steer','brake'])
cav_info.to_csv('./experience_data/cav_info.csv',index=False)
hdv_info = pd.DataFrame(HDV_infos,columns=['veh_id','episode','episode_step','px','py','sx','sy','ax','ay'])
hdv_info.to_csv('./experience_data/hdv_info.csv',index=False)
if env and env.world is not None:
env.world.destroy()
# env.world.destroy_all_actors()
# env.sych_distroy()
print('\ndisabling synchronous mode.')
settings = env._carla_world.get_settings()
settings.synchronous_mode = False
env._carla_world.apply_settings(settings)
pygame.quit()
from tqdm import tqdm
from carla_env import CarlaEnv
SYNCMODE = True
NUM_PLAYERS = 1
if __name__ == "__main__":
print("Run.py")
if not SYNCMODE:
env = CarlaEnv(sync=SYNCMODE, num_players=NUM_PLAYERS)
print("Env activated")
env.reset()
for i in tqdm(range(10000)):
# print("IN Step ", i)
env.step()
# time.sleep(0.2)
else:
with CarlaEnv(sync=SYNCMODE, num_players=NUM_PLAYERS) as env:
print("Env activated")
env.reset()
for i in tqdm(range(10000)):
# print("IN Step ", i)
env.step()
overwrite_experiment = args['overwrite_experiment']
directory_to_save = './_benchmarks_results/{}'.format(curr_town)
#if os.path.exists(directory_to_save):
# if overwrite_experiment:
# print("Removing {}".format(directory_to_save))
# os.system("rm -rf {}".format(directory_to_save))
# else:
# print("ERROR: A directory called {} already exists.".format(directory_to_save))
# print("Please make sure to move the contents as running this program will overwrite the contents of this directory.")
# exit()
now = time.time()
print("Loading the Imitition Network and performing one simulation run for the baseline path..")
os.system("mkdir -p _benchmarks_results")
env = CarlaEnv(town=curr_town, task=curr_task, port=curr_port, save_images=False, gpu_num=curr_gpu, adversary_name=curr_adversary_name, intersection=curr_intersection)
print("Complete.")
baseSteer = env.baseline_steer # get the steering angles for the baseline run
MAX_LEN = int(len(env.baseline_steer)*.8) # set maximum number of frames to 80 percent of baseline scenario
baseSteer = baseSteer[:MAX_LEN] # subset steering angles to maximum number of allowed frames
def target(rot1,pos1,width1,length1):
# specify our attack (in this case double black lines) as a dictionary to pass to the CarlaEnv object.
# TODO: get the num_para
numbers = {}
adversary_properties = ['rot','pos','length','width','r','g','b']
adversary_name = "rectangle1"
import sys
import time
import random
import pickle
sys.path.insert(0, '/home/wael/Desktop/golfcart/GEME6-CARLA/Carla_Gym/envs/')
from carla_env import CarlaEnv
import numpy as np
import matplotlib.pyplot as plt
from controller import Controller
# env = CarlaEnv()
while True:
try:
env = CarlaEnv()
break
except Exception as e:
print(e)
max_episodes = 100
max_steps = 1800
control = Controller()
for i in range(int(max_episodes)):
s = env.reset()
counter = 0
while True:
if counter == 0:
a = control.action(s[0], s[3], s[1], 0.001, controller_type="LQR")
summary_vars[1]:
ep_ave_max_q / float(j)
})
writer.add_summary(summary_str, i)
writer.flush()
print('| Reward: {:d} | Episode: {:d} | Qmax: {:.4f}'.format(int(ep_reward), \
i, (ep_ave_max_q / float(j))))
break
last_info = info
while True:
try:
env = CarlaEnv()
break
except Exception as e:
print(e)
with tf.Session() as sess:
action_bound = env.action_space.high
Actor = ActorNetwork(sess=sess,
state_dim=env.observation_space.shape[0],
action_dim=env.action_space.shape[0],
action_bound=action_bound,
learning_rate=0.003,
tau=.125,
batch_size=128)
Critic = CriticNetwork(sess=sess,
state_dim=env.observation_space.shape[0],
action_dim=env.action_space.shape[0],
directory_to_save = './_benchmarks_results/{}'.format(curr_town)
if os.path.exists(directory_to_save):
if overwrite_experiment:
print("Removing {}".format(directory_to_save))
os.system("rm -rf {}".format(directory_to_save))
else:
print("ERROR: A directory called {} already exists.".format(directory_to_save))
print("Please make sure to move the contents as running this program will overwrite the contents of this directory.")
exit()
now = time.time()
print("Loading the Imitition Network and performing one simulation run for the baseline path..")
os.system("mkdir -p _benchmarks_results")
env = CarlaEnv(task=curr_task, town='Town01_nemesisA', scene=curr_scene,
port=curr_port, save_images=False, gpu_num=curr_gpu)
print("Complete.")
baseSteer = env.baseline_steer # get the steering angles for the baseline run
MAX_LEN = int(len(env.baseline_steer)*.8) # set maximum number of frames to 80 percent of baseline scenario
baseSteer = baseSteer[:MAX_LEN] # subset steering angles to maximum number of allowed frames
def target(pos1, rot1, pos2=0, rot2=0, width=10, length=200, colorR=0, colorG=0, colorB=0):
# specify our attack (in this case double black lines) as a dictionary to pass to the CarlaEnv object.
dict_params = {
# the first line
0:{
'pos': int(pos1),
'rot': rot1,
os.system("rm -rf {}".format(directory_to_save))
else:
print("ERROR: A directory called {} already exists.".format(
directory_to_save))
print(
"Please make sure to move the contents to a new location as running this program will overwrite the contents of this directory."
)
exit()
os.system("mkdir -p _benchmarks_results")
print(
"Loading the Imitition Network and performing one simulation run for the target path.."
)
env = CarlaEnv(task=target_task,
town=curr_town,
scene=target_scene,
port=curr_port,
save_images=False,
gpu_num=curr_gpu)
print("Complete.")
targetSteer = env.get_steer() # get the steering angles for the target run
MAX_LEN = int(
len(env.get_steer()) *
.8) # set maximum number of frames to 80 percent of target scenario
targetSteer = targetSteer[:
MAX_LEN] # subset steering angles to maximum number of allowed frames
env.task = baseline_task
env.scene = baseline_scene
env.experiment_name = 'baseline'