Carla自动驾驶强化学习carla强化学习

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我是靠谱客的博主无聊蛋挞，这篇文章主要介绍Carla自动驾驶强化学习carla强化学习，现在分享给大家，希望可以做个参考。

Carla自动驾驶强化学习

carla强化学习

carla强化学习

来自youtube大神的代码。
自己首先复制一下项目旨在跑通，然后再精读。
自己在tf2.0环境下一直没有跑通，于是改为
因为自己下载的carla版本限制了python只能用3.7

复制代码

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conda create
-n carla python=3.7
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple
tensorflow==1.14
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple
keras==2.2.5

复制代码

import glob
import os
import sys
import random
import time
import numpy as np
import cv2
import math
from collections import deque
from keras.applications.xception import Xception
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.models import Model
from keras.callbacks import TensorBoard
import tensorflow as tf
import keras.backend.tensorflow_backend as backend
from threading import Thread
from tqdm import tqdm
try:
sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
sys.version_info.major,
sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass
import carla
SHOW_PREVIEW = False
IM_WIDTH = 640
IM_HEIGHT = 480
SECONDS_PER_EPISODE = 10
REPLAY_MEMORY_SIZE = 5_000
MIN_REPLAY_MEMORY_SIZE = 1_000
MINIBATCH_SIZE = 16
PREDICTION_BATCH_SIZE = 1
TRAINING_BATCH_SIZE = MINIBATCH_SIZE // 4
UPDATE_TARGET_EVERY = 5
MODEL_NAME = "Xception"
MEMORY_FRACTION = 0.4
MIN_REWARD = -200
EPISODES = 100
DISCOUNT = 0.99
epsilon = 1
EPSILON_DECAY = 0.95 ## 0.9975 99975
MIN_EPSILON = 0.001
AGGREGATE_STATS_EVERY = 10
# Own Tensorboard class
class ModifiedTensorBoard(TensorBoard):
# Overriding init to set initial step and writer (we want one log file for all .fit() calls)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.step = 1
self.writer = tf.summary.FileWriter(self.log_dir)
# Overriding this method to stop creating default log writer
def set_model(self, model):
pass
# Overrided, saves logs with our step number
# (otherwise every .fit() will start writing from 0th step)
def on_epoch_end(self, epoch, logs=None):
self.update_stats(**logs)
# Overrided
# We train for one batch only, no need to save anything at epoch end
def on_batch_end(self, batch, logs=None):
pass
# Overrided, so won't close writer
def on_train_end(self, _):
pass
# Custom method for saving own metrics
# Creates writer, writes custom metrics and closes writer
def update_stats(self, **stats):
self._write_logs(stats, self.step)
class CarEnv:
SHOW_CAM = SHOW_PREVIEW
STEER_AMT = 1.0
im_width = IM_WIDTH
im_height = IM_HEIGHT
front_camera = None
def __init__(self):
self.client = carla.Client("localhost", 2000)
self.client.set_timeout(2.0)
self.world = self.client.get_world()
self.blueprint_library = self.world.get_blueprint_library()
self.model_3 = self.blueprint_library.filter("model3")[0]
def reset(self):
self.collision_hist = []
self.actor_list = []
self.transform = random.choice(self.world.get_map().get_spawn_points())
self.vehicle = self.world.spawn_actor(self.model_3, self.transform)
self.actor_list.append(self.vehicle)
self.rgb_cam = self.blueprint_library.find('sensor.camera.rgb')
self.rgb_cam.set_attribute("image_size_x", f"{self.im_width}")
self.rgb_cam.set_attribute("image_size_y", f"{self.im_height}")
self.rgb_cam.set_attribute("fov", f"110")
transform = carla.Transform(carla.Location(x=2.5, z=0.7))
self.sensor = self.world.spawn_actor(self.rgb_cam, transform, attach_to=self.vehicle)
self.actor_list.append(self.sensor)
self.sensor.listen(lambda data: self.process_img(data))
self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))
time.sleep(4)
colsensor = self.blueprint_library.find("sensor.other.collision")
self.colsensor = self.world.spawn_actor(colsensor, transform, attach_to=self.vehicle)
self.actor_list.append(self.colsensor)
self.colsensor.listen(lambda event: self.collision_data(event))
while self.front_camera is None:
time.sleep(0.01)
self.episode_start = time.time()
self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))
return self.front_camera
def collision_data(self, event):
self.collision_hist.append(event)
def process_img(self, image):
i = np.array(image.raw_data)
#print(i.shape)
i2 = i.reshape((self.im_height, self.im_width, 4))
i3 = i2[:, :, :3]
if self.SHOW_CAM:
cv2.imshow("", i3)
cv2.waitKey(1)
self.front_camera = i3
def step(self, action):
if action == 0:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=-1*self.STEER_AMT))
elif action == 1:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer= 0))
elif action == 2:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=1*self.STEER_AMT))
v = self.vehicle.get_velocity()
kmh = int(3.6 * math.sqrt(v.x**2 + v.y**2 + v.z**2))
if len(self.collision_hist) != 0:
done = True
reward = -200
elif kmh < 50:
done = False
reward = -1
else:
done = False
reward = 1
if self.episode_start + SECONDS_PER_EPISODE < time.time():
done = True
return self.front_camera, reward, done, None
class DQNAgent:
def __init__(self):
self.model = self.create_model()
self.target_model = self.create_model()
self.target_model.set_weights(self.model.get_weights())
self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE)
self.tensorboard = ModifiedTensorBoard(log_dir=f"logs/{MODEL_NAME}-{int(time.time())}")
self.target_update_counter = 0
self.graph = tf.get_default_graph()
self.terminate = False
self.last_logged_episode = 0
self.training_initialized = False
def create_model(self):
base_model = Xception(weights=None, include_top=False, input_shape=(IM_HEIGHT, IM_WIDTH,3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(3, activation="linear")(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=["accuracy"])
return model
def update_replay_memory(self, transition):
# transition = (current_state, action, reward, new_state, done)
self.replay_memory.append(transition)
def train(self):
if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE:
return
minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE)
current_states = np.array([transition[0] for transition in minibatch])/255
with self.graph.as_default():
current_qs_list = self.model.predict(current_states, PREDICTION_BATCH_SIZE)
new_current_states = np.array([transition[3] for transition in minibatch])/255
with self.graph.as_default():
future_qs_list = self.target_model.predict(new_current_states, PREDICTION_BATCH_SIZE)
X = []
y = []
for index, (current_state, action, reward, new_state, done) in enumerate(minibatch):
if not done:
max_future_q = np.max(future_qs_list[index])
new_q = reward + DISCOUNT * max_future_q
else:
new_q = reward
current_qs = current_qs_list[index]
current_qs[action] = new_q
X.append(current_state)
y.append(current_qs)
log_this_step = False
if self.tensorboard.step > self.last_logged_episode:
log_this_step = True
self.last_log_episode = self.tensorboard.step
with self.graph.as_default():
self.model.fit(np.array(X)/255, np.array(y), batch_size=TRAINING_BATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if log_this_step else None)
if log_this_step:
self.target_update_counter += 1
if self.target_update_counter > UPDATE_TARGET_EVERY:
self.target_model.set_weights(self.model.get_weights())
self.target_update_counter = 0
def get_qs(self, state):
return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0]
def train_in_loop(self):
X = np.random.uniform(size=(1, IM_HEIGHT, IM_WIDTH, 3)).astype(np.float32)
y = np.random.uniform(size=(1, 3)).astype(np.float32)
with self.graph.as_default():
self.model.fit(X,y, verbose=False, batch_size=1)
self.training_initialized = True
while True:
if self.terminate:
return
self.train()
time.sleep(0.01)
if __name__ == '__main__':
FPS = 60
# For stats
ep_rewards = [-200]
# For more repetitive results
random.seed(1)
np.random.seed(1)
tf.set_random_seed(1)
# Memory fraction, used mostly when trai8ning multiple agents
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION)
backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)))
# Create models folder
if not os.path.isdir('models'):
os.makedirs('models')
# Create agent and environment
agent = DQNAgent()
env = CarEnv()
# Start training thread and wait for training to be initialized
trainer_thread = Thread(target=agent.train_in_loop, daemon=True)
trainer_thread.start()
while not agent.training_initialized:
time.sleep(0.01)
# Initialize predictions - forst prediction takes longer as of initialization that has to be done
# It's better to do a first prediction then before we start iterating over episode steps
agent.get_qs(np.ones((env.im_height, env.im_width, 3)))
# Iterate over episodes
for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'):
#try:
env.collision_hist = []
# Update tensorboard step every episode
agent.tensorboard.step = episode
# Restarting episode - reset episode reward and step number
episode_reward = 0
step = 1
# Reset environment and get initial state
current_state = env.reset()
# Reset flag and start iterating until episode ends
done = False
episode_start = time.time()
# Play for given number of seconds only
while True:
# This part stays mostly the same, the change is to query a model for Q values
if np.random.random() > epsilon:
# Get action from Q table
action = np.argmax(agent.get_qs(current_state))
else:
# Get random action
action = np.random.randint(0, 3)
# This takes no time, so we add a delay matching 60 FPS (prediction above takes longer)
time.sleep(1/FPS)
new_state, reward, done, _ = env.step(action)
# Transform new continous state to new discrete state and count reward
episode_reward += reward
# Every step we update replay memory
agent.update_replay_memory((current_state, action, reward, new_state, done))
current_state = new_state
step += 1
if done:
break
# End of episode - destroy agents
for actor in env.actor_list:
actor.destroy()
# Append episode reward to a list and log stats (every given number of episodes)
ep_rewards.append(episode_reward)
if not episode % AGGREGATE_STATS_EVERY or episode == 1:
average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:])
min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:])
max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:])
agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon)
# Save model, but only when min reward is greater or equal a set value
if min_reward >= MIN_REWARD:
agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')
# Decay epsilon
if epsilon > MIN_EPSILON:
epsilon *= EPSILON_DECAY
epsilon = max(MIN_EPSILON, epsilon)
# Set termination flag for training thread and wait for it to finish
agent.terminate = True
trainer_thread.join()
agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')

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import glob
import os
import sys
import random
import time
import numpy as np
import cv2
import math
from collections import deque
from keras.applications.xception import Xception
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.models import Model
from keras.callbacks import TensorBoard
import tensorflow as tf
import keras.backend.tensorflow_backend as backend
from threading import Thread
from tqdm import tqdm
try:
sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
sys.version_info.major,
sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass
import carla
SHOW_PREVIEW = False
IM_WIDTH = 640
IM_HEIGHT = 480
SECONDS_PER_EPISODE = 10
REPLAY_MEMORY_SIZE = 5_000
MIN_REPLAY_MEMORY_SIZE = 1_000
MINIBATCH_SIZE = 16
PREDICTION_BATCH_SIZE = 1
TRAINING_BATCH_SIZE = MINIBATCH_SIZE // 4
UPDATE_TARGET_EVERY = 5
MODEL_NAME = "Xception"
MEMORY_FRACTION = 0.4
MIN_REWARD = -200
EPISODES = 100
DISCOUNT = 0.99
epsilon = 1
EPSILON_DECAY = 0.95 ## 0.9975 99975
MIN_EPSILON = 0.001
AGGREGATE_STATS_EVERY = 10
# Own Tensorboard class
class ModifiedTensorBoard(TensorBoard):
# Overriding init to set initial step and writer (we want one log file for all .fit() calls)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.step = 1
self.writer = tf.summary.FileWriter(self.log_dir)
# Overriding this method to stop creating default log writer
def set_model(self, model):
pass
# Overrided, saves logs with our step number
# (otherwise every .fit() will start writing from 0th step)
def on_epoch_end(self, epoch, logs=None):
self.update_stats(**logs)
# Overrided
# We train for one batch only, no need to save anything at epoch end
def on_batch_end(self, batch, logs=None):
pass
# Overrided, so won't close writer
def on_train_end(self, _):
pass
# Custom method for saving own metrics
# Creates writer, writes custom metrics and closes writer
def update_stats(self, **stats):
self._write_logs(stats, self.step)
class CarEnv:
SHOW_CAM = SHOW_PREVIEW
STEER_AMT = 1.0
im_width = IM_WIDTH
im_height = IM_HEIGHT
front_camera = None
def __init__(self):
self.client = carla.Client("localhost", 2000)
self.client.set_timeout(2.0)
self.world = self.client.get_world()
self.blueprint_library = self.world.get_blueprint_library()
self.model_3 = self.blueprint_library.filter("model3")[0]
def reset(self):
self.collision_hist = []
self.actor_list = []
self.transform = random.choice(self.world.get_map().get_spawn_points())
self.vehicle = self.world.spawn_actor(self.model_3, self.transform)
self.actor_list.append(self.vehicle)
self.rgb_cam = self.blueprint_library.find('sensor.camera.rgb')
self.rgb_cam.set_attribute("image_size_x", f"{self.im_width}")
self.rgb_cam.set_attribute("image_size_y", f"{self.im_height}")
self.rgb_cam.set_attribute("fov", f"110")
transform = carla.Transform(carla.Location(x=2.5, z=0.7))
self.sensor = self.world.spawn_actor(self.rgb_cam, transform, attach_to=self.vehicle)
self.actor_list.append(self.sensor)
self.sensor.listen(lambda data: self.process_img(data))
self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))
time.sleep(4)
colsensor = self.blueprint_library.find("sensor.other.collision")
self.colsensor = self.world.spawn_actor(colsensor, transform, attach_to=self.vehicle)
self.actor_list.append(self.colsensor)
self.colsensor.listen(lambda event: self.collision_data(event))
while self.front_camera is None:
time.sleep(0.01)
self.episode_start = time.time()
self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))
return self.front_camera
def collision_data(self, event):
self.collision_hist.append(event)
def process_img(self, image):
i = np.array(image.raw_data)
#print(i.shape)
i2 = i.reshape((self.im_height, self.im_width, 4))
i3 = i2[:, :, :3]
if self.SHOW_CAM:
cv2.imshow("", i3)
cv2.waitKey(1)
self.front_camera = i3
def step(self, action):
if action == 0:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=-1*self.STEER_AMT))
elif action == 1:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer= 0))
elif action == 2:
self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=1*self.STEER_AMT))
v = self.vehicle.get_velocity()
kmh = int(3.6 * math.sqrt(v.x**2 + v.y**2 + v.z**2))
if len(self.collision_hist) != 0:
done = True
reward = -200
elif kmh < 50:
done = False
reward = -1
else:
done = False
reward = 1
if self.episode_start + SECONDS_PER_EPISODE < time.time():
done = True
return self.front_camera, reward, done, None
class DQNAgent:
def __init__(self):
self.model = self.create_model()
self.target_model = self.create_model()
self.target_model.set_weights(self.model.get_weights())
self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE)
self.tensorboard = ModifiedTensorBoard(log_dir=f"logs/{MODEL_NAME}-{int(time.time())}")
self.target_update_counter = 0
self.graph = tf.get_default_graph()
self.terminate = False
self.last_logged_episode = 0
self.training_initialized = False
def create_model(self):
base_model = Xception(weights=None, include_top=False, input_shape=(IM_HEIGHT, IM_WIDTH,3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(3, activation="linear")(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=["accuracy"])
return model
def update_replay_memory(self, transition):
# transition = (current_state, action, reward, new_state, done)
self.replay_memory.append(transition)
def train(self):
if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE:
return
minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE)
current_states = np.array([transition[0] for transition in minibatch])/255
with self.graph.as_default():
current_qs_list = self.model.predict(current_states, PREDICTION_BATCH_SIZE)
new_current_states = np.array([transition[3] for transition in minibatch])/255
with self.graph.as_default():
future_qs_list = self.target_model.predict(new_current_states, PREDICTION_BATCH_SIZE)
X = []
y = []
for index, (current_state, action, reward, new_state, done) in enumerate(minibatch):
if not done:
max_future_q = np.max(future_qs_list[index])
new_q = reward + DISCOUNT * max_future_q
else:
new_q = reward
current_qs = current_qs_list[index]
current_qs[action] = new_q
X.append(current_state)
y.append(current_qs)
log_this_step = False
if self.tensorboard.step > self.last_logged_episode:
log_this_step = True
self.last_log_episode = self.tensorboard.step
with self.graph.as_default():
self.model.fit(np.array(X)/255, np.array(y), batch_size=TRAINING_BATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if log_this_step else None)
if log_this_step:
self.target_update_counter += 1
if self.target_update_counter > UPDATE_TARGET_EVERY:
self.target_model.set_weights(self.model.get_weights())
self.target_update_counter = 0
def get_qs(self, state):
return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0]
def train_in_loop(self):
X = np.random.uniform(size=(1, IM_HEIGHT, IM_WIDTH, 3)).astype(np.float32)
y = np.random.uniform(size=(1, 3)).astype(np.float32)
with self.graph.as_default():
self.model.fit(X,y, verbose=False, batch_size=1)
self.training_initialized = True
while True:
if self.terminate:
return
self.train()
time.sleep(0.01)
if __name__ == '__main__':
FPS = 60
# For stats
ep_rewards = [-200]
# For more repetitive results
random.seed(1)
np.random.seed(1)
tf.set_random_seed(1)
# Memory fraction, used mostly when trai8ning multiple agents
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION)
backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)))
# Create models folder
if not os.path.isdir('models'):
os.makedirs('models')
# Create agent and environment
agent = DQNAgent()
env = CarEnv()
# Start training thread and wait for training to be initialized
trainer_thread = Thread(target=agent.train_in_loop, daemon=True)
trainer_thread.start()
while not agent.training_initialized:
time.sleep(0.01)
# Initialize predictions - forst prediction takes longer as of initialization that has to be done
# It's better to do a first prediction then before we start iterating over episode steps
agent.get_qs(np.ones((env.im_height, env.im_width, 3)))
# Iterate over episodes
for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'):
#try:
env.collision_hist = []
# Update tensorboard step every episode
agent.tensorboard.step = episode
# Restarting episode - reset episode reward and step number
episode_reward = 0
step = 1
# Reset environment and get initial state
current_state = env.reset()
# Reset flag and start iterating until episode ends
done = False
episode_start = time.time()
# Play for given number of seconds only
while True:
# This part stays mostly the same, the change is to query a model for Q values
if np.random.random() > epsilon:
# Get action from Q table
action = np.argmax(agent.get_qs(current_state))
else:
# Get random action
action = np.random.randint(0, 3)
# This takes no time, so we add a delay matching 60 FPS (prediction above takes longer)
time.sleep(1/FPS)
new_state, reward, done, _ = env.step(action)
# Transform new continous state to new discrete state and count reward
episode_reward += reward
# Every step we update replay memory
agent.update_replay_memory((current_state, action, reward, new_state, done))
current_state = new_state
step += 1
if done:
break
# End of episode - destroy agents
for actor in env.actor_list:
actor.destroy()
# Append episode reward to a list and log stats (every given number of episodes)
ep_rewards.append(episode_reward)
if not episode % AGGREGATE_STATS_EVERY or episode == 1:
average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:])
min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:])
max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:])
agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon)
# Save model, but only when min reward is greater or equal a set value
if min_reward >= MIN_REWARD:
agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')
# Decay epsilon
if epsilon > MIN_EPSILON:
epsilon *= EPSILON_DECAY
epsilon = max(MIN_EPSILON, epsilon)
# Set termination flag for training thread and wait for it to finish
agent.terminate = True
trainer_thread.join()
agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')